This is an R Markdown Notebook. When you execute code within the notebook, the results appear beneath the code.

Add a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Cmd+Option+I.

library(raster)
size_dat=read.table("/Users/stijnhantson/Documents/projects/VIIRS_ros/fire_growth_5days_v4.txt", header=T, row.names=NULL)

size_dat=as.data.frame(size_dat)
size_dat=size_dat[-1,]
size_dat=size_dat[,-1]
colnames(size_dat)=c("firename","year","cause","size1","size2","size3","size4","size5","final_firesize","mean_precip1","mean_precip2","mean_precip3","mean_precip4","mean_precip5","mean_tmax1","mean_tmax2","mean_tmax3","mean_tmax4","mean_tmax5","mean_tmean1","mean_tmean2","mean_tmean3","mean_tmean4","mean_tmean5","mean_vpdmax1","mean_vpdmax2","mean_vpdmax3","mean_vpdmax4","mean_vpdmax5","mean_windspeed1","mean_windspeed2","mean_windspeed3","mean_windspeed4","mean_windspeed5","landcover","ecosystem","biomass","elevation")

size_dat2 <- data.frame(lapply(size_dat, function(x) as.numeric(as.character(x))))
NAs introduced by coercion
size_dat2$human = 0
size_dat2$human[size_dat2$cause !=1 & size_dat2$cause !=14 & size_dat2$cause !=17]=1
size_dat2$human[size_dat2$cause ==1 ]=2

pro1 =size_dat2[which(size_dat2$human == 2 & size_dat2$landcover == 1 ), ]
pro2 =size_dat2[which(size_dat2$human == 1 & size_dat2$landcover == 1 ), ]

t.test(pro1$size1,pro2$size1)

    Welch Two Sample t-test

data:  pro1$size1 and pro2$size1
t = -2.0397, df = 74.381, p-value = 0.04493
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -22.0986230  -0.2593373
sample estimates:
mean of x mean of y 
 3.056385 14.235365 
t.test(pro1$size2,pro2$size2)

    Welch Two Sample t-test

data:  pro1$size2 and pro2$size2
t = -2.758, df = 73.138, p-value = 0.007341
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -32.702045  -5.266208
sample estimates:
mean of x mean of y 
 8.254394 27.238521 
t.test(pro1$size3,pro2$size3)

    Welch Two Sample t-test

data:  pro1$size3 and pro2$size3
t = -2.9002, df = 58.203, p-value = 0.005254
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -51.965993  -9.526741
sample estimates:
mean of x mean of y 
 14.10484  44.85121 
t.test(pro1$size4,pro2$size4)

    Welch Two Sample t-test

data:  pro1$size4 and pro2$size4
t = -3.1797, df = 53.078, p-value = 0.002461
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -74.99798 -16.98040
sample estimates:
mean of x mean of y 
 17.68430  63.67349 
t.test(pro1$size5,pro2$size5)

    Welch Two Sample t-test

data:  pro1$size5 and pro2$size5
t = -3.4218, df = 39.443, p-value = 0.001462
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -117.71273  -30.26844
sample estimates:
mean of x mean of y 
 19.71795  93.70853 
pro =size_dat2[which(size_dat2$human == 1 & size_dat2$landcover == 1), ]
length(pro$year)
[1] 68
boxplot(pro$size1,pro$size2,pro$size3,pro$size4,pro$size5,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,500), cex.lab=1.4,cex.axis = 1.3)


pro =size_dat2[which(size_dat2$human == 2 & size_dat2$landcover == 1), ]
length(pro$year)
[1] 77
boxplot(pro$size1,pro$size2,pro$size3,pro$size4,pro$size5,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,500), cex.lab=1.4,cex.axis = 1.3)


pro =size_dat2[which(size_dat2$human == 1 & size_dat2$landcover == 2), ]
length(pro$year)
[1] 18
boxplot(pro$size1,pro$size2,pro$size3,pro$size4,pro$size5,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,500), cex.lab=1.4,cex.axis = 1.3)


pro =size_dat2[which(size_dat2$human == 2 & size_dat2$landcover == 2), ]
length(pro$year)
[1] 9
boxplot(pro$size1,pro$size2,pro$size3,pro$size4,pro$size5,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,500), cex.lab=1.4,cex.axis = 1.3)

length(!is.na(pro$size5))
[1] 9
pro =size_dat2[which(size_dat2$human == 1 & size_dat2$ecosystem == 6), ]
length(pro$year)
[1] 41
boxplot(pro$size1,pro$size2,pro$size3,pro$size4,pro$size5,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,500), cex.lab=1.4,cex.axis = 1.3)


pro =size_dat2[which(size_dat2$human == 2& size_dat2$ecosystem == 6), ]
length(pro$year)
[1] 79
boxplot(pro$size1,pro$size2,pro$size3,pro$size4,pro$size5,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,500), cex.lab=1.4,cex.axis = 1.3)

NA
NA

plot fire size map QGIS

library(data.table)
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     
data.table 1.13.0 using 1 threads (see ?getDTthreads).  Latest news: r-datatable.com
**********
This installation of data.table has not detected OpenMP support. It should still work but in single-threaded mode.
This is a Mac. Please read https://mac.r-project.org/openmp/. Please engage with Apple and ask them for support. Check r-datatable.com for updates, and our Mac instructions here: https://github.com/Rdatatable/data.table/wiki/Installation. After several years of many reports of installation problems on Mac, it's time to gingerly point out that there have been no similar problems on Windows or Linux.
**********

Attaching package: ‘data.table’

The following object is masked from ‘package:raster’:

    shift
DT= data.table(res)
fire_size = DT[ , .SD[which.min(growth_km)], by = firename]
Error in .checkTypos(e, names_x) : 
  Object 'growth_km' not found amongst bi, erc, etr, fm100, fm1000 and 23 more
extract number and size statistics from frap

library(raster)

dr1 =shapefile("/Users/stijnhantson/Documents/data/FRAP/fire18_1.shp")
dr1$YEAR_=as.numeric(as.character(dr1$YEAR_))
dr1$Shape_Area=as.numeric(as.character(dr1$Shape_Area))
dr1$ALARM_DATE = as.Date(dr1$ALARM_DATE )
dr1=dr1[!is.na(dr1$YEAR_), ]
dr1=dr1[dr1$YEAR_>2011,]


dr2=dr1[dr1$YEAR_ == 2013,]
plot(table(dr2$ALARM_DATE), xlim=c(as.Date("2013/01/01"),as.Date("2013/12/31")))

table(factor(dr2$ALARM_DATE, levels = as.Date("2013/01/01"):as.Date("2013/12/31")))

15706 15707 15708 15709 15710 15711 15712 15713 15714 15715 15716 15717 15718 15719 15720 
    0     0     0     0     0     0     0     0     0     0     0     0     0     0     0 
15721 15722 15723 15724 15725 15726 15727 15728 15729 15730 15731 15732 15733 15734 15735 
    0     0     0     0     0     0     0     0     0     0     0     0     0     0     0 
15736 15737 15738 15739 15740 15741 15742 15743 15744 15745 15746 15747 15748 15749 15750 
    0     0     0     0     0     0     0     0     0     0     0     0     0     0     0 
15751 15752 15753 15754 15755 15756 15757 15758 15759 15760 15761 15762 15763 15764 15765 
    0     0     0     0     0     0     0     0     0     0     0     0     0     0     0 
15766 15767 15768 15769 15770 15771 15772 15773 15774 15775 15776 15777 15778 15779 15780 
    0     0     0     0     0     0     0     0     0     0     0     0     0     0     0 
15781 15782 15783 15784 15785 15786 15787 15788 15789 15790 15791 15792 15793 15794 15795 
    0     0     0     0     0     0     0     0     0     0     0     0     0     0     0 
15796 15797 15798 15799 15800 15801 15802 15803 15804 15805 15806 15807 15808 15809 15810 
    0     0     0     0     0     0     0     0     0     0     0     0     0     0     0 
15811 15812 15813 15814 15815 15816 15817 15818 15819 15820 15821 15822 15823 15824 15825 
    0     0     0     0     0     0     0     0     0     0     0     0     0     0     0 
15826 15827 15828 15829 15830 15831 15832 15833 15834 15835 15836 15837 15838 15839 15840 
    0     0     0     0     0     0     0     0     0     0     0     0     0     0     0 
15841 15842 15843 15844 15845 15846 15847 15848 15849 15850 15851 15852 15853 15854 15855 
    0     0     0     0     0     0     0     0     0     0     0     0     0     0     0 
15856 15857 15858 15859 15860 15861 15862 15863 15864 15865 15866 15867 15868 15869 15870 
    0     0     0     0     0     0     0     0     0     0     0     0     0     0     0 
15871 15872 15873 15874 15875 15876 15877 15878 15879 15880 15881 15882 15883 15884 15885 
    0     0     0     0     0     0     0     0     0     0     0     0     0     0     0 
15886 15887 15888 15889 15890 15891 15892 15893 15894 15895 15896 15897 15898 15899 15900 
    0     0     0     0     0     0     0     0     0     0     0     0     0     0     0 
15901 15902 15903 15904 15905 15906 15907 15908 15909 15910 15911 15912 15913 15914 15915 
    0     0     0     0     0     0     0     0     0     0     0     0     0     0     0 
15916 15917 15918 15919 15920 15921 15922 15923 15924 15925 15926 15927 15928 15929 15930 
    0     0     0     0     0     0     0     0     0     0     0     0     0     0     0 
15931 15932 15933 15934 15935 15936 15937 15938 15939 15940 15941 15942 15943 15944 15945 
    0     0     0     0     0     0     0     0     0     0     0     0     0     0     0 
15946 15947 15948 15949 15950 15951 15952 15953 15954 15955 15956 15957 15958 15959 15960 
    0     0     0     0     0     0     0     0     0     0     0     0     0     0     0 
15961 15962 15963 15964 15965 15966 15967 15968 15969 15970 15971 15972 15973 15974 15975 
    0     0     0     0     0     0     0     0     0     0     0     0     0     0     0 
15976 15977 15978 15979 15980 15981 15982 15983 15984 15985 15986 15987 15988 15989 15990 
    0     0     0     0     0     0     0     0     0     0     0     0     0     0     0 
15991 15992 15993 15994 15995 15996 15997 15998 15999 16000 16001 16002 16003 16004 16005 
    0     0     0     0     0     0     0     0     0     0     0     0     0     0     0 
16006 16007 16008 16009 16010 16011 16012 16013 16014 16015 16016 16017 16018 16019 16020 
    0     0     0     0     0     0     0     0     0     0     0     0     0     0     0 
16021 16022 16023 16024 16025 16026 16027 16028 16029 16030 16031 16032 16033 16034 16035 
    0     0     0     0     0     0     0     0     0     0     0     0     0     0     0 
16036 16037 16038 16039 16040 16041 16042 16043 16044 16045 16046 16047 16048 16049 16050 
    0     0     0     0     0     0     0     0     0     0     0     0     0     0     0 
16051 16052 16053 16054 16055 16056 16057 16058 16059 16060 16061 16062 16063 16064 16065 
    0     0     0     0     0     0     0     0     0     0     0     0     0     0     0 
16066 16067 16068 16069 16070 
    0     0     0     0     0 

human lightning unknown ratio through time



library(raster)
library(rgdal)
package ‘rgdal’ was built under R version 3.6.2rgdal: version: 1.5-12, (SVN revision 1018)
Geospatial Data Abstraction Library extensions to R successfully loaded
Loaded GDAL runtime: GDAL 2.4.2, released 2019/06/28
Path to GDAL shared files: /Library/Frameworks/R.framework/Versions/3.6/Resources/library/rgdal/gdal
GDAL binary built with GEOS: FALSE 
Loaded PROJ runtime: Rel. 5.2.0, September 15th, 2018, [PJ_VERSION: 520]
Path to PROJ shared files: /Library/Frameworks/R.framework/Versions/3.6/Resources/library/rgdal/proj
Linking to sp version:1.4-2
Overwritten PROJ_LIB was /Library/Frameworks/R.framework/Versions/3.6/Resources/library/rgdal/proj
library(rgeos)
package ‘rgeos’ was built under R version 3.6.2rgeos version: 0.5-3, (SVN revision 634)
 GEOS runtime version: 3.7.2-CAPI-1.11.2 
 Linking to sp version: 1.4-1 
 Polygon checking: TRUE 
library(sf)
frap=readOGR("/Users/stijnhantson/Documents/data/FRAP/fire19_1.shp")
OGR data source with driver: ESRI Shapefile 
Source: "/Users/stijnhantson/Documents/data/FRAP/fire19_1.shp", layer: "fire19_1"
with 20820 features
It has 18 fields
Integer64 fields read as strings:  CAUSE C_METHOD OBJECTIVE 
shape = shapefile("/Users/stijnhantson/Documents/data/veg_california/ca_eco_l3/ca_eco_l3.shp")
shape = spTransform(shape,crs(frap))


frap = st_make_valid(st_read("/Users/stijnhantson/Documents/data/FRAP/fire19_1.shp"))
Reading layer `fire19_1' from data source `/Users/stijnhantson/Documents/data/FRAP/fire19_1.shp' using driver `ESRI Shapefile'
GDAL Message 1: organizePolygons() received an unexpected geometry.  Either a polygon with interior rings, or a polygon with less than 4 points, or a non-Polygon geometry.  Return arguments as a collection.GDAL Message 1: Geometry of polygon of fid 19691 cannot be translated to Simple Geometry. All polygons will be contained in a multipolygon.
Simple feature collection with 20820 features and 18 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -373237.5 ymin: -604727.6 xmax: 519987.8 ymax: 518283.7
CRS:            3310
types <- vapply(sf::st_geometry(frap), function(x) {
  class(x)[2]
}, "")
polys <- frap[ grepl("*POLYGON", types), ]

frap=sf:::as_Spatial(polys)
frap=gBuffer(frap, byid=T,  width=0.0)
eco_inter=intersect(frap,shape)
NA
  1. frap
  2. only fire growth datasets
  • prepare final dataset to open

library(raster)
#library(rgdal)
daily_res=read.table("/Users/stijnhantson/Documents/projects/VIIRS_ros/final_dataset_V5.txt",header=T)

res=as.data.frame(daily_res)

res$mean_ros =as.numeric(as.character(res$mean_ros))
res$max_ros =as.numeric(as.character(res$max_ros))
res$median95_ros =as.numeric(as.character(res$median95_ros))
res$bi =as.numeric(as.character(res$bi))
res$erc =as.numeric(as.character(res$erc))
res$etr =as.numeric(as.character(res$etr))
res$fm100 =as.numeric(as.character(res$fm100))
res$fm1000 =as.numeric(as.character(res$fm1000))
res$pet =as.numeric(as.character(res$pet))
res$pr =as.numeric(as.character(res$pr))
res$rmax =as.numeric(as.character(res$rmax))
res$rmin =as.numeric(as.character(res$rmin))
res$th =as.numeric(as.character(res$th))
res$tmmn =as.numeric(as.character(res$tmmn))
res$tmmx =as.numeric(as.character(res$tmmx))
res$vpd =as.numeric(as.character(res$vpd))
#res$ws =as.numeric(as.character(res$ws))
res$vs =as.numeric(as.character(res$vs))
res$growth =as.numeric(as.character(res$growth))
res$total_area =as.numeric(as.character(res$total_area))
res$mean_frp =as.numeric(as.character(res$mean_frp))
res$frp_95 =as.numeric(as.character(res$frp_95))
res$max_land =as.numeric(as.character(res$max_land))
res$mean_land =as.numeric(as.character(res$mean_land))
res$biomass =as.numeric(as.character(res$biomass))
res$year =as.numeric(as.character(res$year))
res$month =as.numeric(as.character(res$month))
res$doy_out =as.numeric(as.character(res$doy_out))

res$mean_dnbr =as.numeric(as.character(res$mean_dnbr))
res$mean_rdnbr =as.numeric(as.character(res$mean_rdnbr))
res$mean_bas =as.numeric(as.character(res$mean_bas))
res$median_dnbr =as.numeric(as.character(res$median_dnbr))
res$median_rdnbr =as.numeric(as.character(res$median_rdnbr))
res$median_bas =as.numeric(as.character(res$median_bas))
res$q95_dnbr =as.numeric(as.character(res$q95_dnbr))
res$q95_rdnbr =as.numeric(as.character(res$q95_rdnbr))
res$q95_bas =as.numeric(as.character(res$q95_bas))


res = res[-1,]
res$per_ba = res$growth/res$total_area
res$growth_km =res$growth/1000000

res$human = 0
res$human[res$cause !=1 & res$cause !=14 & res$cause !=17]=1
res$human[res$cause ==1 ]=2

res$ros_km = (res$median95_ros*24)/1000
res$ros_mean_km = (res$mean_ros*24)/1000

is there relation between days untill 75% and fire size

dr1 =shapefile("/Users/stijnhantson/Documents/data/FRAP/fire18_1.shp")
dr1$YEAR_=as.numeric(as.character(dr1$YEAR_))
dr1$Shape_Area=as.numeric(as.character(dr1$Shape_Area))
dr1=dr1[!is.na(dr1$YEAR_), ]
dr1=dr1[dr1$YEAR_>2011,]

fi = dr1[dr1$OBJECTID==17495,]
dr1=subset(dr1, GIS_ACRES>300)

summary(dr1)
Object of class SpatialPolygonsDataFrame
Coordinates:
        min      max
x -332881.6 349023.3
y -601475.0 462306.3
Is projected: TRUE 
proj4string :
[+proj=aea +lat_1=34 +lat_2=40.5 +lat_0=0 +lon_0=-120 +x_0=0 +y_0=-4000000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0
+units=m +no_defs]
Data attributes:
    OBJECTID         YEAR_         STATE              AGENCY            UNIT_ID           FIRE_NAME           INC_NUM         
 Min.   :11970   Min.   :2012   Length:515         Length:515         Length:515         Length:515         Length:515        
 1st Qu.:17777   1st Qu.:2014   Class :character   Class :character   Class :character   Class :character   Class :character  
 Median :19129   Median :2016   Mode  :character   Mode  :character   Mode  :character   Mode  :character   Mode  :character  
 Mean   :19125   Mean   :2015                                                                                                 
 3rd Qu.:20512   3rd Qu.:2017                                                                                                 
 Max.   :21127   Max.   :2018                                                                                                 
                                                                                                                              
  ALARM_DATE         CONT_DATE            CAUSE             COMMENTS           REPORT_AC          GIS_ACRES       
 Length:515         Length:515         Length:515         Length:515         Min.   :     0.0   Min.   :   303.8  
 Class :character   Class :character   Class :character   Class :character   1st Qu.:   504.1   1st Qu.:   612.4  
 Mode  :character   Mode  :character   Mode  :character   Mode  :character   Median :  1651.5   Median :  1707.6  
                                                                             Mean   : 12021.3   Mean   : 12138.1  
                                                                             3rd Qu.:  6970.0   3rd Qu.:  7068.0  
                                                                             Max.   :410203.0   Max.   :410202.5  
                                                                             NA's   :67                           
   C_METHOD          OBJECTIVE           FIRE_NUM           Shape_Leng       Shape_Area       
 Length:515         Length:515         Length:515         Min.   :  4635   Min.   :1.229e+06  
 Class :character   Class :character   Class :character   1st Qu.: 10017   1st Qu.:2.478e+06  
 Mode  :character   Mode  :character   Mode  :character   Median : 17475   Median :6.910e+06  
                                                          Mean   : 41493   Mean   :4.912e+07  
                                                          3rd Qu.: 37905   3rd Qu.:2.860e+07  
                                                          Max.   :445282   Max.   :1.660e+09  
                                                                                              
res75 = res[res$per_ba > 0.999,]

peak_day1 = as.data.frame(aggregate(res75$fire_day, by = list(res75$firename,res75$year), min))
p=13
year1=0
first=0
last=0
len_d=0
day75=0
firenam1=0
name_fire=0
size=0
k=0
for (p in 1:(length(peak_day1$Group.1)))
{
  print(p)
  year = peak_day1[p,2]
    firenam = as.character(peak_day1[p,1]) 
  
  fir = dr1[which(dr1$YEAR_==year & dr1$FIRE_NAME==firenam),]
  if (length(fir)>1){
    maxsize = max(fir$GIS_ACRES)
    fir = fir[fir$GIS_ACRES ==  maxsize,]
  }
  
  if (!is.na(fir$ALARM_DATE)  | !is.na(fir$ALARM_DATE)){
    k=k+1
    day75[k] = peak_day1[p,3]
    firenam1[k]=firenam 
  year1[k] = year
  first[k]= fir$ALARM_DATE
  last[k] = fir$CONT_DATE
  len_d[k] = as.numeric(as.Date(fir$CONT_DATE) -as.Date(fir$ALARM_DATE))
 name_fire[k]= fir$OBJECTID
    size[k] = fir$GIS_ACRES
  }
}
[1] 1
[1] 2
[1] 3
[1] 4
[1] 5
[1] 6
[1] 7
[1] 8
[1] 9
[1] 10
[1] 11
[1] 12
[1] 13
[1] 14
[1] 15
[1] 16
[1] 17
[1] 18
[1] 19
[1] 20
[1] 21
[1] 22
[1] 23
[1] 24
[1] 25
[1] 26
[1] 27
[1] 28
[1] 29
[1] 30
[1] 31
[1] 32
[1] 33
[1] 34
[1] 35
[1] 36
[1] 37
[1] 38
[1] 39
[1] 40
[1] 41
[1] 42
[1] 43
[1] 44
[1] 45
[1] 46
[1] 47
[1] 48
[1] 49
[1] 50
[1] 51
[1] 52
[1] 53
[1] 54
[1] 55
[1] 56
[1] 57
[1] 58
[1] 59
[1] 60
[1] 61
[1] 62
[1] 63
[1] 64
[1] 65
[1] 66
[1] 67
[1] 68
[1] 69
[1] 70
[1] 71
[1] 72
[1] 73
[1] 74
[1] 75
[1] 76
[1] 77
[1] 78
[1] 79
[1] 80
[1] 81
[1] 82
[1] 83
[1] 84
[1] 85
[1] 86
[1] 87
[1] 88
[1] 89
[1] 90
[1] 91
[1] 92
[1] 93
[1] 94
[1] 95
[1] 96
[1] 97
[1] 98
[1] 99
[1] 100
[1] 101
[1] 102
[1] 103
[1] 104
[1] 105
[1] 106
[1] 107
[1] 108
[1] 109
[1] 110
[1] 111
[1] 112
[1] 113
[1] 114
[1] 115
[1] 116
[1] 117
[1] 118
[1] 119
[1] 120
[1] 121
[1] 122
[1] 123
[1] 124
[1] 125
[1] 126
[1] 127
[1] 128
[1] 129
[1] 130
[1] 131
[1] 132
[1] 133
[1] 134
[1] 135
[1] 136
[1] 137
[1] 138
[1] 139
[1] 140
[1] 141
[1] 142
[1] 143
[1] 144
[1] 145
[1] 146
[1] 147
[1] 148
[1] 149
[1] 150
[1] 151
[1] 152
[1] 153
[1] 154
[1] 155
[1] 156
[1] 157
[1] 158
[1] 159
[1] 160
[1] 161
[1] 162
[1] 163
[1] 164
[1] 165
[1] 166
[1] 167
[1] 168
[1] 169
[1] 170
[1] 171
[1] 172
[1] 173
[1] 174
[1] 175
[1] 176
[1] 177
[1] 178
[1] 179
[1] 180
[1] 181
[1] 182
[1] 183
[1] 184
[1] 185
[1] 186
[1] 187
[1] 188
[1] 189
[1] 190
[1] 191
[1] 192
[1] 193
[1] 194
[1] 195
[1] 196
[1] 197
[1] 198
[1] 199
[1] 200
[1] 201
[1] 202
[1] 203
[1] 204
[1] 205
[1] 206
[1] 207
[1] 208
[1] 209
[1] 210
[1] 211
[1] 212
plot(len_d,day75, xlab="fire duration FRAP (days)", ylab="fire duration VIIRS (days)")

plot mean vs max fire rate-of-spread

#summary(res)
plot(res$ros_km,res$ros_mean_km, xlab="maximum fire-spread-rate (km/day",ylab="mean fire-spread-rate (km/day)")


pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/mean_vs_max_ros_v1.pdf", width = 7, height = 7)
plot(res$ros_km,res$ros_mean_km, xlim=c(0,25),ylim=c(0,10), xlab="fire rate-of-spread (km/day)",ylab="mean fire rate-of-spread (km/day)", cex.lab=1.3,cex.axis = 1.25)
dev.off()
quartz_off_screen 
                2 

difference between human and lightnign fires

me=0
me1=0
days = c("day1","day2","day3","day4","day5")
days1 = c("1","2","3","4","5")
pro1 = res[res$fire_day == 1 & res$human == 1,]
pro2 = res[res$fire_day == 2 & res$human == 1,]
pro3 = res[res$fire_day == 3 & res$human == 1,]
pro4 = res[res$fire_day == 4 & res$human == 1,]
pro5 = res[res$fire_day == 5 & res$human == 1,]
me[1] =mean(pro1$growth_km,na.omit=T)
me[2] =mean(pro2$growth_km,na.omit=T)
me[3] =mean(pro3$growth_km,na.omit=T)
me[4] =mean(pro4$growth_km,na.omit=T)
me[5] =mean(pro5$growth_km,na.omit=T)

pro1h = res[res$fire_day == 1 & res$human == 2,]
pro2h = res[res$fire_day == 2 & res$human == 2,]
pro3h = res[res$fire_day == 3 & res$human == 2,]
pro4h = res[res$fire_day == 4 & res$human == 2,]
pro5h = res[res$fire_day == 5 & res$human == 2,]
me1[1] =mean(pro1h$growth_km,na.omit=T)
me1[2] =mean(pro2h$growth_km,na.omit=T)
me1[3] =mean(pro3h$growth_km,na.omit=T)
me1[4] =mean(pro4h$growth_km,na.omit=T)
me1[5] =mean(pro5h$growth_km,na.omit=T)

boxplot(pro1$growth_km,pro2$growth_km,pro3$growth_km,pro4$growth_km,pro5$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,250), cex.lab=1.4,cex.axis = 1.3)


boxplot(pro1h$growth_km,pro2h$growth_km,pro3h$growth_km,pro4h$growth_km,pro5h$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,250), cex.lab=1.4,cex.axis = 1.3)

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/figure1_v2.pdf", width = 10, height = 5)
par(mfrow=c(1,2))
par(mar=c(4, 4, 1,0.1))
par(mgp=c(2.3,1,0))
boxplot(pro1$growth_km,pro2$growth_km,pro3$growth_km,pro4$growth_km,pro5$growth_km,names=c("1","2","3","4","5"),xlab="Day since fire start",ylab= expression('Fire size (km'^2*')'),ylim=c(0,200), cex.lab=1.4,cex.axis = 1.3)
text(0.3,195,"a)",pos=4, cex=1.4)
boxplot(pro1h$growth_km,pro2h$growth_km,pro3h$growth_km,pro4h$growth_km,pro5h$growth_km,names=c("1","2","3","4","5"),xlab="Day since fire start",ylab= "",ylim=c(0,200), cex.lab=1.4,cex.axis = 1.3)
text(0.3,195,"b)",pos=4, cex=1.4)
dev.off()
quartz_off_screen 
                3 
pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_figure1_v2.pdf", width = 10, height = 5)
par(mfrow=c(1,2))
par(mar=c(4, 4, 1,0.1))
par(mgp=c(2.3,1,0))
boxplot(pro1$growth_km,pro2$growth_km,pro3$growth_km,pro4$growth_km,pro5$growth_km,names=c("1","2","3","4","5"),xlab="Day since fire start",ylab= expression('Fire size (km'^2*')'),ylim=c(0,700), cex.lab=1.4,cex.axis = 1.3)
text(0.3,690,"a)",pos=4, cex=1.4)
boxplot(pro1h$growth_km,pro2h$growth_km,pro3h$growth_km,pro4h$growth_km,pro5h$growth_km,names=c("1","2","3","4","5"),xlab="Day since fire start",ylab= "",ylim=c(0,700), cex.lab=1.4,cex.axis = 1.3)
text(0.3,690,"b)",pos=4, cex=1.4)
dev.off() 
quartz_off_screen 
                3 
pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig4/all_v3.pdf", width = 4, height = 5)
par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n',cex.axis=1.4, lty = 1, lwd = 2)
axis(side=1, at=c(1:5),labels=days1,cex.axis=1.4)
points(me1,type="o",lty = 2, lwd = 2)
legend(x="topleft", legend=c("human","lightning"),col="black",lty = c(1,2),pch=1,bty = "n",lwd = 2,cex=1.5, pt.cex = 1)

dev.off() 
quartz_off_screen 
                3 

t.test(log(pro1$growth_km),log(pro1h$growth_km))

    Welch Two Sample t-test

data:  log(pro1$growth_km) and log(pro1h$growth_km)
t = 6.5083, df = 169.36, p-value = 8.265e-10
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 1.492386 2.791866
sample estimates:
 mean of x  mean of y 
 1.3226886 -0.8194377 
t.test(log(pro2$growth_km),log(pro2h$growth_km))

    Welch Two Sample t-test

data:  log(pro2$growth_km) and log(pro2h$growth_km)
t = 6.9843, df = 134.4, p-value = 1.199e-10
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 1.369607 2.451706
sample estimates:
mean of x mean of y 
2.7559564 0.8453003 
t.test(log(pro3$growth_km),log(pro3h$growth_km))

    Welch Two Sample t-test

data:  log(pro3$growth_km) and log(pro3h$growth_km)
t = 5.3694, df = 137.17, p-value = 3.286e-07
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.9293526 2.0129234
sample estimates:
mean of x mean of y 
 3.186372  1.715234 
t.test(log(pro4$growth_km),log(pro4h$growth_km))

    Welch Two Sample t-test

data:  log(pro4$growth_km) and log(pro4h$growth_km)
t = 4.6217, df = 122.92, p-value = 9.467e-06
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.6909157 1.7261008
sample estimates:
mean of x mean of y 
 3.513204  2.304696 
t.test(log(pro5$growth_km),log(pro5h$growth_km))

    Welch Two Sample t-test

data:  log(pro5$growth_km) and log(pro5h$growth_km)
t = 4.9328, df = 103.98, p-value = 3.089e-06
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.9170094 2.1499736
sample estimates:
mean of x mean of y 
 3.895266  2.361774 

##################3 for western cordillera ecoregion ##################

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig4/north_v2.pdf", width = 4, height = 5)
par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n', lty = 1, lwd = 2,cex.lab=1.2,cex.axis=1.4)
axis(side=1, at=c(1:5),labels=days1,cex.axis=1.4)
points(me1,type="o",lty = 2, lwd = 2)
dev.off() 
null device 
          1 

for mediteranean california

pro1 = res[res$fire_day == 1 & res$human == 1 & (res$eco1 == 11),]
pro2 = res[res$fire_day == 2 & res$human == 1 & (res$eco1 == 11),]
pro3 = res[res$fire_day == 3 & res$human == 1 & (res$eco1 == 11),]
pro4 = res[res$fire_day == 4 & res$human == 1 & (res$eco1 == 11),]
pro5 = res[res$fire_day == 5 & res$human == 1 & (res$eco1 == 11),]

boxplot(pro1$growth_km,pro2$growth_km,pro3$growth_km,pro4$growth_km,pro5$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)


pro1h = res[res$fire_day == 1 & res$human == 2 & (res$eco1 == 11),]
pro2h = res[res$fire_day == 2 & res$human == 2 & (res$eco1 == 11),]
pro3h = res[res$fire_day == 3 & res$human == 2 & (res$eco1 == 11),]
pro4h = res[res$fire_day == 4 & res$human == 2 & (res$eco1 == 11),]
pro5h = res[res$fire_day == 5 & res$human == 2 & (res$eco1 == 11),]

boxplot(pro1h$growth_km,pro2h$growth_km,pro3h$growth_km,pro4h$growth_km,pro5h$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)

me[1] =mean(pro1$growth_km,na.omit=T)
me[2] =mean(pro2$growth_km,na.omit=T)
me[3] =mean(pro3$growth_km,na.omit=T)
me[4] =mean(pro4$growth_km,na.omit=T)
me[5] =mean(pro5$growth_km,na.omit=T)

me1[1] =mean(pro1h$growth_km,na.rm=T)
me1[2] =mean(pro2h$growth_km,na.rm=T)
me1[3] =mean(pro3h$growth_km,na.rm=T)
me1[4] =mean(pro4h$growth_km,na.rm=T)
me1[5] =mean(pro5h$growth_km,na.rm=T)


par(mgp=c(2.3,1,0))  

plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n', lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days)
points(me1,type="o",lty = 2, lwd = 2)

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig4/med_v2.pdf", width = 4, height = 5)
par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="",cex.axis=1.4, xaxt='n', lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days1,cex.axis=1.4)
points(me1,type="o",lty = 2, lwd = 2)
dev.off() 
quartz_off_screen 
                3 

for difference in autumn

pro1 = res[res$fire_day == 1 & res$human == 1 & res$month >8,]
pro2 = res[res$fire_day == 2 & res$human == 1 & res$month >8,]
pro3 = res[res$fire_day == 3 & res$human == 1 & res$month >8,]
pro4 = res[res$fire_day == 4 & res$human == 1 & res$month >8,]
pro5 = res[res$fire_day == 5 & res$human == 1 & res$month >8,]

boxplot(pro1$growth_km,pro2$growth_km,pro3$growth_km,pro4$growth_km,pro5$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)


pro1h = res[res$fire_day == 1 & res$human == 2 & res$month >8,]
pro2h = res[res$fire_day == 2 & res$human == 2 & res$month >8,]
pro3h = res[res$fire_day == 3 & res$human == 2 & res$month >8,]
pro4h = res[res$fire_day == 4 & res$human == 2 & res$month >8,]
pro5h = res[res$fire_day == 5 & res$human == 2 & res$month >8,]

boxplot(pro1h$growth_km,pro2h$growth_km,pro3h$growth_km,pro4h$growth_km,pro5h$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)

me[1] =mean(pro1$growth_km,na.omit=T)
me[2] =mean(pro2$growth_km,na.omit=T)
me[3] =mean(pro3$growth_km,na.omit=T)
me[4] =mean(pro4$growth_km,na.omit=T)
me[5] =mean(pro5$growth_km,na.omit=T)

me1[1] =mean(pro1h$growth_km,na.rm=T)
me1[2] =mean(pro2h$growth_km,na.rm=T)
me1[3] =mean(pro3h$growth_km,na.rm=T)
me1[4] =mean(pro4h$growth_km,na.rm=T)
me1[5] =mean(pro5h$growth_km,na.rm=T)


par(mgp=c(2.3,1,0))  

plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n', lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days)
points(me1,type="o",lty = 2, lwd = 2)

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig4/autumn_v2.pdf", width = 4, height = 5)
par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,250),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n',cex.axis=1.4, lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days1,cex.axis=1.4)
points(me1,type="o",lty = 2, lwd = 2)
dev.off() 
quartz_off_screen 
                3 

for difference in summer

pro1 = res[res$fire_day == 1 & res$human == 1 & res$month <=8 ,]
pro2 = res[res$fire_day == 2 & res$human == 1 & res$month <=8,]
pro3 = res[res$fire_day == 3 & res$human == 1 & res$month <=8,]
pro4 = res[res$fire_day == 4 & res$human == 1 & res$month <=8,]
pro5 = res[res$fire_day == 5 & res$human == 1 & res$month <=8,]

boxplot(pro1$growth_km,pro2$growth_km,pro3$growth_km,pro4$growth_km,pro5$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)


pro1h = res[res$fire_day == 1 & res$human == 2 & res$month <=8,]
pro2h = res[res$fire_day == 2 & res$human == 2 & res$month <=8,]
pro3h = res[res$fire_day == 3 & res$human == 2 & res$month <=8,]
pro4h = res[res$fire_day == 4 & res$human == 2 & res$month <=8,]
pro5h = res[res$fire_day == 5 & res$human == 2 & res$month <=8,]

boxplot(pro1h$growth_km,pro2h$growth_km,pro3h$growth_km,pro4h$growth_km,pro5h$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)

me[1] =mean(pro1$growth_km,na.omit=T)
me[2] =mean(pro2$growth_km,na.omit=T)
me[3] =mean(pro3$growth_km,na.omit=T)
me[4] =mean(pro4$growth_km,na.omit=T)
me[5] =mean(pro5$growth_km,na.omit=T)

me1[1] =mean(pro1h$growth_km,na.rm=T)
me1[2] =mean(pro2h$growth_km,na.rm=T)
me1[3] =mean(pro3h$growth_km,na.rm=T)
me1[4] =mean(pro4h$growth_km,na.rm=T)
me1[5] =mean(pro5h$growth_km,na.rm=T)


par(mgp=c(2.3,1,0))  

plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n', lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days)
points(me1,type="o",lty = 2, lwd = 2)

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig4/summer_v2.pdf", width = 4, height = 5)
par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n',cex.axis=1.4, lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days1,cex.axis=1.4)
points(me1,type="o",lty = 2, lwd = 2)
dev.off() 
quartz_off_screen 
                3 

for difference in summer in western cordillera

pro1 = res[res$fire_day == 1 & res$human == 1 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro2 = res[res$fire_day == 2 & res$human == 1 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro3 = res[res$fire_day == 3 & res$human == 1 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro4 = res[res$fire_day == 4 & res$human == 1 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro5 = res[res$fire_day == 5 & res$human == 1 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 6 | res$eco1 == 7),]

boxplot(pro1$growth_km,pro2$growth_km,pro3$growth_km,pro4$growth_km,pro5$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)


pro1h = res[res$fire_day == 1 & res$human == 2 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro2h = res[res$fire_day == 2 & res$human == 2 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro3h = res[res$fire_day == 3 & res$human == 2 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro4h = res[res$fire_day == 4 & res$human == 2 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro5h = res[res$fire_day == 5 & res$human == 2 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 6 | res$eco1 == 7),]

boxplot(pro1h$growth_km,pro2h$growth_km,pro3h$growth_km,pro4h$growth_km,pro5h$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)

t.test(log(pro1$growth_km),log(pro1h$growth_km))

    Welch Two Sample t-test

data:  log(pro1$growth_km) and log(pro1h$growth_km)
t = 1.038, df = 53.703, p-value = 0.3039
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.4317907  1.3585976
sample estimates:
 mean of x  mean of y 
-0.2855046 -0.7489080 
t.test(log(pro2$growth_km),log(pro2h$growth_km))

    Welch Two Sample t-test

data:  log(pro2$growth_km) and log(pro2h$growth_km)
t = 3.6912, df = 82.447, p-value = 0.0003995
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.555548 1.854073
sample estimates:
mean of x mean of y 
 2.058908  0.854097 
t.test(log(pro3$growth_km),log(pro3h$growth_km))

    Welch Two Sample t-test

data:  log(pro3$growth_km) and log(pro3h$growth_km)
t = 2.2426, df = 64.873, p-value = 0.02835
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.07573427 1.30867147
sample estimates:
mean of x mean of y 
 2.535161  1.842958 
t.test(log(pro4$growth_km),log(pro4h$growth_km))

    Welch Two Sample t-test

data:  log(pro4$growth_km) and log(pro4h$growth_km)
t = 2.3845, df = 62.683, p-value = 0.02014
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.1141261 1.2961201
sample estimates:
mean of x mean of y 
 2.976315  2.271192 
t.test(log(pro5$growth_km),log(pro5h$growth_km))

    Welch Two Sample t-test

data:  log(pro5$growth_km) and log(pro5h$growth_km)
t = 2.3428, df = 40.216, p-value = 0.02417
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.1060498 1.4368888
sample estimates:
mean of x mean of y 
 3.327351  2.555881 
me[1] =mean(pro1$growth_km,na.omit=T)
me[2] =mean(pro2$growth_km,na.omit=T)
me[3] =mean(pro3$growth_km,na.omit=T)
me[4] =mean(pro4$growth_km,na.omit=T)
me[5] =mean(pro5$growth_km,na.omit=T)

me1[1] =mean(pro1h$growth_km,na.rm=T)
me1[2] =mean(pro2h$growth_km,na.rm=T)
me1[3] =mean(pro3h$growth_km,na.rm=T)
me1[4] =mean(pro4h$growth_km,na.rm=T)
me1[5] =mean(pro5h$growth_km,na.rm=T)

par(mgp=c(2.3,1,0))  

plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n', lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days)
points(me1,type="o",lty = 2, lwd = 2)

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig4/north_summer_v2.pdf", width = 4, height = 5)
par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="",cex.axis=1.4, xaxt='n', lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days1,cex.axis=1.4)
points(me1,type="o",lty = 2, lwd = 2)
dev.off() 
quartz_off_screen 
                3 

for difference in autumn in western cordillera

pro1 = res[res$fire_day == 1 & res$human == 1 & ( res$month >8 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro2 = res[res$fire_day == 2 & res$human == 1 & ( res$month >8 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro3 = res[res$fire_day == 3 & res$human == 1 & ( res$month >8 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro4 = res[res$fire_day == 4 & res$human == 1 & ( res$month >8 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro5 = res[res$fire_day == 5 & res$human == 1 & ( res$month >8 ) & (res$eco1 == 6 | res$eco1 == 7),]

boxplot(pro1$growth_km,pro2$growth_km,pro3$growth_km,pro4$growth_km,pro5$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)


pro1h = res[res$fire_day == 1 & res$human == 2 & ( res$month >8 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro2h = res[res$fire_day == 2 & res$human == 2 & ( res$month >8 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro3h = res[res$fire_day == 3 & res$human == 2 & ( res$month >8 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro4h = res[res$fire_day == 4 & res$human == 2 & (res$month >8 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro5h = res[res$fire_day == 5 & res$human == 2 & ( res$month >8 ) & (res$eco1 == 6 | res$eco1 == 7),]

boxplot(pro1h$growth_km,pro2h$growth_km,pro3h$growth_km,pro4h$growth_km,pro5h$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)

me[1] =mean(pro1$growth_km,na.omit=T)
me[2] =mean(pro2$growth_km,na.omit=T)
me[3] =mean(pro3$growth_km,na.omit=T)
me[4] =mean(pro4$growth_km,na.omit=T)
me[5] =mean(pro5$growth_km,na.omit=T)

me1[1] =mean(pro1h$growth_km,na.rm=T)
me1[2] =mean(pro2h$growth_km,na.rm=T)
me1[3] =mean(pro3h$growth_km,na.rm=T)
me1[4] =mean(pro4h$growth_km,na.rm=T)
me1[5] =mean(pro5h$growth_km,na.rm=T)

par(mgp=c(2.3,1,0))  

plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n', lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days)
points(me1,type="o",lty = 2, lwd = 2)

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig4/north_autumn_v2.pdf", width = 4, height = 5)
par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,250),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n',cex.axis=1.4, lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days1,cex.axis=1.4)
points(me1,type="o",lty = 2, lwd = 2)
dev.off() 
quartz_off_screen 
                3 

for difference in summer in meditereanean

pro1 = res[res$fire_day == 1 & res$human == 1 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 11),]
pro2 = res[res$fire_day == 2 & res$human == 1 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 11),]
pro3 = res[res$fire_day == 3 & res$human == 1 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 11),]
pro4 = res[res$fire_day == 4 & res$human == 1 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 11),]
pro5 = res[res$fire_day == 5 & res$human == 1 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 11),]

boxplot(pro1$growth_km,pro2$growth_km,pro3$growth_km,pro4$growth_km,pro5$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)


pro1h = res[res$fire_day == 1 & res$human == 2 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 11),]
pro2h = res[res$fire_day == 2 & res$human == 2 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 11),]
pro3h = res[res$fire_day == 3 & res$human == 2 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 11),]
pro4h = res[res$fire_day == 4 & res$human == 2 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 11),]
pro5h = res[res$fire_day == 5 & res$human == 2 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 11),]

boxplot(pro1h$growth_km,pro2h$growth_km,pro3h$growth_km,pro4h$growth_km,pro5h$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)

#t.test(log(pro1$growth_km),log(pro1h$growth_km))
#t.test(log(pro2$growth_km),log(pro2h$growth_km))
#t.test(log(pro3$growth_km),log(pro3h$growth_km))
#t.test(log(pro4$growth_km),log(pro4h$growth_km))
#t.test(log(pro5$growth_km),log(pro5h$growth_km))

me[1] =mean(pro1$growth_km,na.omit=T)
me[2] =mean(pro2$growth_km,na.omit=T)
me[3] =mean(pro3$growth_km,na.omit=T)
me[4] =mean(pro4$growth_km,na.omit=T)
me[5] =mean(pro5$growth_km,na.omit=T)

me1[1] =mean(pro1h$growth_km,na.rm=T)
me1[2] =mean(pro2h$growth_km,na.rm=T)
me1[3] =mean(pro3h$growth_km,na.rm=T)
me1[4] =mean(pro4h$growth_km,na.rm=T)
me1[5] =mean(pro5h$growth_km,na.rm=T)

par(mgp=c(2.3,1,0))  

plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n', lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days)
points(me1,type="o",lty = 2, lwd = 2)

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig4/med_summer_v2.pdf", width = 4, height = 5)
par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n',cex.axis=1.4, lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days1,cex.axis=1.4)
points(me1,type="o",lty = 2, lwd = 2)
dev.off() 
quartz_off_screen 
                3 

` ################## for difference in autumn in mediteranean ##################

pro1 = res[res$fire_day == 1 & res$human == 1 & ( res$month >8 ) & (res$eco1 == 11),]
pro2 = res[res$fire_day == 2 & res$human == 1 & ( res$month >8 ) & (res$eco1 == 11),]
pro3 = res[res$fire_day == 3 & res$human == 1 & ( res$month >8 ) & (res$eco1 == 11),]
pro4 = res[res$fire_day == 4 & res$human == 1 & ( res$month >8 ) & (res$eco1 == 11),]
pro5 = res[res$fire_day == 5 & res$human == 1 & ( res$month >8 ) & (res$eco1 == 11),]

boxplot(pro1$growth_km,pro2$growth_km,pro3$growth_km,pro4$growth_km,pro5$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)


pro1h = res[res$fire_day == 1 & res$human == 2 & ( res$month >8 ) & (res$eco1 == 11),]
pro2h = res[res$fire_day == 2 & res$human == 2 & ( res$month >8 ) & (res$eco1 == 11),]
pro3h = res[res$fire_day == 3 & res$human == 2 & ( res$month >8 ) & (res$eco1 == 11),]
pro4h = res[res$fire_day == 4 & res$human == 2 & ( res$month >8 ) & (res$eco1 == 11),]
pro5h = res[res$fire_day == 5 & res$human == 2 & ( res$month >8 ) & (res$eco1 == 11),]

boxplot(pro1h$growth_km,pro2h$growth_km,pro3h$growth_km,pro4h$growth_km,pro5h$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)

me[1] =mean(pro1$growth_km,na.omit=T)
me[2] =mean(pro2$growth_km,na.omit=T)
me[3] =mean(pro3$growth_km,na.omit=T)
me[4] =mean(pro4$growth_km,na.omit=T)
me[5] =mean(pro5$growth_km,na.omit=T)

me1[1] =mean(pro1h$growth_km,na.rm=T)
me1[2] =mean(pro2h$growth_km,na.rm=T)
me1[3] =mean(pro3h$growth_km,na.rm=T)
me1[4] =mean(pro4h$growth_km,na.rm=T)
me1[5] =mean(pro5h$growth_km,na.rm=T)

par(mgp=c(2.3,1,0))  

plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n', lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days)
points(me1,type="o",lty = 2, lwd = 2)

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig4/med_autumn_v2.pdf", width = 4, height = 5)
par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,400),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n', lty = 1,cex.axis=1.4, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days1,cex.axis=1.4)
points(me1,type="o",lty = 2, lwd = 2)
dev.off() 
quartz_off_screen 
                3 

how many days does it take to reach 75% burnt area

res75 = res[res$per_ba > 0.75,]
#peak_day = as.data.frame(aggregate(res75$fire_day, by = list(res75$firename,res75$cause), min))
#peak_day=subset(peak_day,x < 55)
#hi = hist(peak_day$x,prob =F, breaks= c(0:54), xlim=c(0,55), ylab="number of fires", xlab="days", cex.lab=1.4,cex.axis=1.3)

out1 = subset(res75,cause == 1 )   #1=lightning; 14=unknown; 7=arson
out2 = subset(res75,cause !=1 & cause != 14 )


peak_day1 = as.data.frame(aggregate(out1$fire_day, by = list(out1$firename), min))
peak_day2 = as.data.frame(aggregate(out2$fire_day, by = list(out2$firename), min))
peak=as.data.frame(aggregate(res75$fire_day, by = list(res75$firename), min))
quantile(peak_day1$x,0.50,type=3) 
50% 
 10 
quantile(peak_day2$x,0.50,type=3) 
50% 
  3 
peak_day1=subset(peak_day1,x < 56)
peak_day2=subset(peak_day2,x < 56)
hist.a =hist(peak_day1$x,breaks =c(0:55),plot=F)
hist.b =hist(peak_day2$x,breaks =c(0:55),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

fr = barplot(fg,xlab="Days after ignition",ylab="Number of fires",cex.lab=1.4,cex.axis = 1.3, xlim=c(1,65), ylim=c(0,30))
axis(1,c(0.7,5.5,11.5,17.5,23.5,29.5,35.5,41.5,47.5,53.5,59.5,65.5),labels=c(1,5,10,15,20,25,30,35,40,45,50,55),cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")

pdf(file="/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/time_to_reach75.pdf",width=7,height=5)
fr = barplot(fg,xlab="Days after ignition",ylab="Number of fires",cex.lab=1.4,cex.axis = 1.3, xlim=c(1,65), ylim=c(0,30))
axis(1,c(0.7,5.5,11.5,17.5,23.5,29.5,35.5,41.5,47.5,53.5,59.5,65.5),labels=c(1,5,10,15,20,25,30,35,40,45,50,55),cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")
dev.off()
quartz_off_screen 
                2 
########  mediteranean  ###########
out1 = subset(res75,cause == 1 & res75$eco1 == 11)   #1=lightning; 14=unknown; 7=arson
out2 = subset(res75,cause !=1 & cause != 14 & res75$eco1 == 11)
peak_day1 = as.data.frame(aggregate(out1$fire_day, by = list(out1$firename), min))
peak_day2 = as.data.frame(aggregate(out2$fire_day, by = list(out2$firename), min))

peak_day1=subset(peak_day1,x < 56)
peak_day2=subset(peak_day2,x < 56)
hist.a =hist(peak_day1$x,breaks =c(0:55),plot=F)
hist.b =hist(peak_day2$x,breaks =c(0:55),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

pdf(file="/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig5_time75/med.pdf",width=7,height=5)
fr = barplot(fg,xlab="Days after ignition",ylab="Number of fires",cex.lab=1.4,cex.axis = 1.3, xlim=c(1,65), ylim=c(0,15))
axis(1,c(0.7,5.5,11.5,17.5,23.5,29.5,35.5,41.5,47.5,53.5,59.5,65.5),labels=c(1,5,10,15,20,25,30,35,40,45,50,55),cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")
dev.off()
quartz_off_screen 
                2 
########  north cal  ###########
out1 = subset(res75,cause == 1  & (res75$eco1 == 6 | res75$eco1 == 7))   #1=lightning; 14=unknown; 7=arson
out2 = subset(res75,cause !=1 & cause != 14  & (res75$eco1 == 6 | res75$eco1 == 7))
peak_day1 = as.data.frame(aggregate(out1$fire_day, by = list(out1$firename), min))
peak_day2 = as.data.frame(aggregate(out2$fire_day, by = list(out2$firename), min))

peak_day1=subset(peak_day1,x < 56)
peak_day2=subset(peak_day2,x < 56)
hist.a =hist(peak_day1$x,breaks =c(0:55),plot=F)
hist.b =hist(peak_day2$x,breaks =c(0:55),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

pdf(file="/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig5_time75/north.pdf",width=7,height=5)
fr = barplot(fg,xlab="Days after ignition",ylab="Number of fires",cex.lab=1.4,cex.axis = 1.3, xlim=c(1,65), ylim=c(0,15))
axis(1,c(0.7,5.5,11.5,17.5,23.5,29.5,35.5,41.5,47.5,53.5,59.5,65.5),labels=c(1,5,10,15,20,25,30,35,40,45,50,55),cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")
dev.off()
quartz_off_screen 
                2 
########  mediteranean  SUMMER ###########
out1 = subset(res75,cause == 1 & res75$eco1 == 11 & ( res75$month <=8 & res75$month >5 ))   #1=lightning; 14=unknown; 7=arson
out2 = subset(res75,cause !=1 & cause != 14 & res75$eco1 == 11 & ( res75$month <=8 & res75$month >5 ))
peak_day1 = as.data.frame(aggregate(out1$fire_day, by = list(out1$firename), min))
peak_day2 = as.data.frame(aggregate(out2$fire_day, by = list(out2$firename), min))

peak_day1=subset(peak_day1,x < 56)
peak_day2=subset(peak_day2,x < 56)
hist.a =hist(peak_day1$x,breaks =c(0:55),plot=F)
hist.b =hist(peak_day2$x,breaks =c(0:55),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

pdf(file="/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig5_time75/med_summer.pdf",width=7,height=5)
fr = barplot(fg,xlab="Days after ignition",ylab="Number of fires",cex.lab=1.4,cex.axis = 1.3, xlim=c(1,65), ylim=c(0,10))
axis(1,c(0.7,5.5,11.5,17.5,23.5,29.5,35.5,41.5,47.5,53.5,59.5,65.5),labels=c(1,5,10,15,20,25,30,35,40,45,50,55),cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")
dev.off()
quartz_off_screen 
                2 
########  north cal  summer ###########
out1 = subset(res75,cause == 1  & (res75$eco1 == 6 | res75$eco1 == 7)& ( res75$month <=8 & res75$month >5 ))   #1=lightning; 14=unknown; 7=arson
out2 = subset(res75,cause !=1 & cause != 14  & (res75$eco1 == 6 | res75$eco1 == 7)& ( res75$month <=8 & res75$month >5 ))
peak_day1 = as.data.frame(aggregate(out1$fire_day, by = list(out1$firename), min))
peak_day2 = as.data.frame(aggregate(out2$fire_day, by = list(out2$firename), min))

peak_day1=subset(peak_day1,x < 56)
peak_day2=subset(peak_day2,x < 56)
hist.a =hist(peak_day1$x,breaks =c(0:55),plot=F)
hist.b =hist(peak_day2$x,breaks =c(0:55),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

pdf(file="/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig5_time75/north_summer.pdf",width=7,height=5)
fr = barplot(fg,xlab="Days after ignition",ylab="Number of fires",cex.lab=1.4,cex.axis = 1.3, xlim=c(1,65), ylim=c(0,12))
axis(1,c(0.7,5.5,11.5,17.5,23.5,29.5,35.5,41.5,47.5,53.5,59.5,65.5),labels=c(1,5,10,15,20,25,30,35,40,45,50,55),cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")
dev.off()
quartz_off_screen 
                2 
########  mediteranean  autumn###########
out1 = subset(res75,cause == 1 & res75$eco1 == 11 & ( res75$month >8))   #1=lightning; 14=unknown; 7=arson
out2 = subset(res75,cause !=1 & cause != 14 & res75$eco1 == 11 & ( res75$month >8 ))
#peak_day1 = as.data.frame(aggregate(out1$fire_day, by = list(out1$firename), min))
peak_day2 = as.data.frame(aggregate(out2$fire_day, by = list(out2$firename), min))

#peak_day1=subset(peak_day1,x < 56)
peak_day2=subset(peak_day2,x < 56)
#hist.a =hist(peak_day1$x,breaks =c(0:55),plot=F)
hist.b =hist(peak_day2$x,breaks =c(0:55),plot=F)
fg = rbind(0,hist.b$counts)

pdf(file="/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig5_time75/med_autumn.pdf",width=7,height=5)
fr = barplot(fg,xlab="Days after ignition",ylab="Number of fires",cex.lab=1.4,cex.axis = 1.3, xlim=c(1,65), ylim=c(0,5))
axis(1,c(0.7,5.5,11.5,17.5,23.5,29.5,35.5,41.5,47.5,53.5,59.5,65.5),labels=c(1,5,10,15,20,25,30,35,40,45,50,55),cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")
dev.off()
quartz_off_screen 
                2 
########  north cal  autumn ###########
out1 = subset(res75,cause == 1  & (res75$eco1 == 6 | res75$eco1 == 7)& ( res75$month >8))   #1=lightning; 14=unknown; 7=arson
out2 = subset(res75,cause !=1 & cause != 14  & (res75$eco1 == 6 | res75$eco1 == 7)& ( res75$month >8 ))
peak_day1 = as.data.frame(aggregate(out1$fire_day, by = list(out1$firename), min))
peak_day2 = as.data.frame(aggregate(out2$fire_day, by = list(out2$firename), min))

peak_day1=subset(peak_day1,x < 56)
peak_day2=subset(peak_day2,x < 56)
hist.a =hist(peak_day1$x,breaks =c(0:55),plot=F)
hist.b =hist(peak_day2$x,breaks =c(0:55),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

pdf(file="/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig5_time75/north_autumn.pdf",width=7,height=5)
fr = barplot(fg,xlab="Days after ignition",ylab="Number of fires",cex.lab=1.4,cex.axis = 1.3, xlim=c(1,65), ylim=c(0,5))
axis(1,c(0.7,5.5,11.5,17.5,23.5,29.5,35.5,41.5,47.5,53.5,59.5,65.5),labels=c(1,5,10,15,20,25,30,35,40,45,50,55),cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")
dev.off()
quartz_off_screen 
                2 


res=res[res$ros_km>0,]
res_f = res[res$max_land == 1,]
res_p = res[res$max_land != 1,]

summary(lm(log(res$mean_frp)~log(res$ros_km)))

Call:
lm(formula = log(res$mean_frp) ~ log(res$ros_km))

Residuals:
    Min      1Q  Median      3Q     Max 
-2.5266 -0.5420 -0.0497  0.4990  4.5702 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)      1.74609    0.01869   93.41   <2e-16 ***
log(res$ros_km)  0.35186    0.01321   26.63   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8362 on 2249 degrees of freedom
  (547 observations deleted due to missingness)
Multiple R-squared:  0.2397,    Adjusted R-squared:  0.2394 
F-statistic: 709.1 on 1 and 2249 DF,  p-value: < 2.2e-16
#just show the plot here
plot(res_f$mean_frp~res_f$ros_km,log="xy",xlim=c(0.005,30),ylim=c(0.1,180),xaxt="n",ylab="mean FRP (MW)",xlab="Rate-of-Spread (km/day)", cex.lab=1.4,cex.axis = 1.3,col="darkgreen")

marks=c(0.01,0.1,1,10)
marks1=c(0.1,0.5,5,50)


pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/fig_FRP_ros_v3.pdf", width = 5, height = 5)
plot(res_f$mean_frp~res_f$ros_km,log="xy",xlim=c(0.005,30),ylim=c(0.1,180),xaxt="n",yaxt="n",ylab="mean FRP (MW)",xlab=expression('Rate-of-Spread (km d'^-1*')'), cex.lab=1.4,cex.axis = 1.3,col="darkgreen")
points(res_p$mean_frp~res_p$ros_km,col="orange")
axis(1,at=marks,labels=marks,cex.axis=1.4 )
axis(2,at=marks1,labels=marks1,cex.axis=1.4 )
legend( x="topleft",legend=c("Forest","Grass & shrub"),col=c("darkgreen","orange"),cex=1.2,pch=1,bty = "n")
dev.off()
quartz_off_screen 
                3 

FRP vs tree mortality

summary(lm(log(res$mean_frp)~res$mean_bas))

Call:
lm(formula = log(res$mean_frp) ~ res$mean_bas)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3250 -0.5824  0.0428  0.5755  2.7943 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.853006   0.039957   21.35   <2e-16 ***
res$mean_bas 0.019447   0.001025   18.97   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8574 on 1727 degrees of freedom
  (1069 observations deleted due to missingness)
Multiple R-squared:  0.1725,    Adjusted R-squared:  0.172 
F-statistic: 359.9 on 1 and 1727 DF,  p-value: < 2.2e-16
summary(lm(data_forest$mean_BA_red~(log10(data_forest$ros_km)+I((log10(data_forest$ros_km))^2))))

Call:
lm(formula = data_forest$mean_BA_red ~ (log10(data_forest$ros_km) + 
    I((log10(data_forest$ros_km))^2)))

Residuals:
    Min      1Q  Median      3Q     Max 
-40.210 -11.114  -3.598   8.209  82.558 

Coefficients:
                                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)                       27.0251     0.5005   54.00   <2e-16 ***
log10(data_forest$ros_km)         25.2022     1.1296   22.31   <2e-16 ***
I((log10(data_forest$ros_km))^2)   8.3470     0.8597    9.71   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 16.51 on 1426 degrees of freedom
Multiple R-squared:  0.2908,    Adjusted R-squared:  0.2898 
F-statistic: 292.4 on 2 and 1426 DF,  p-value: < 2.2e-16

difference in fire size for first 5 days across california and both ecosystems

 
res$ros1 = res$max_ros+1


out1 = subset(res,cause == 1 )   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,cause !=1 & cause != 14 )

hum1 = out2[out2$fire_day ==1,]
hum2 = out2[out2$fire_day ==2,]
hum3 = out2[out2$fire_day ==3,]
hum4 = out2[out2$fire_day ==4,]
hum5 = out2[out2$fire_day ==5,]
lig1 = out1[out1$fire_day ==1,]
lig2 = out1[out1$fire_day ==2,]
lig3 = out1[out1$fire_day ==3,]
lig4 = out1[out1$fire_day ==4,]
lig5 = out1[out1$fire_day ==5,]
mean(hum1$growth)
mean(hum2$growth)
mean(hum3$growth)
mean(hum4$growth)
mean(hum5$growth)
mean(lig1$growth)
mean(lig2$growth)
mean(lig3$growth)
mean(lig4$growth)
mean(lig5$growth)
t.test(log10(hum1$growth),log10(lig1$growth))
t.test(log10(hum2$growth),log10(lig2$growth))
t.test(log10(hum3$growth),log10(lig3$growth))
t.test(log10(hum4$growth),log10(lig4$growth))
t.test(log10(hum5$growth),log10(lig5$growth))

hum1 = out2[out2$fire_day ==1 & out2$eco1==6,]
hum2 = out2[out2$fire_day ==2 & out2$eco1==6,]
hum3 = out2[out2$fire_day ==3 & out2$eco1==6,]
hum4 = out2[out2$fire_day ==4 & out2$eco1==6,]
hum5 = out2[out2$fire_day ==5 & out2$eco1==6,]
lig1 = out1[out1$fire_day ==1 & out1$eco1==6,]
lig2 = out1[out1$fire_day ==2 & out1$eco1==6,]
lig3 = out1[out1$fire_day ==3 & out1$eco1==6,]
lig4 = out1[out1$fire_day ==4 & out1$eco1==6,]
lig5 = out1[out1$fire_day ==5 & out1$eco1==6,]
mean(hum1$growth)
mean(hum2$growth)
mean(hum3$growth)
mean(hum4$growth)
mean(hum5$growth)
mean(lig1$growth, na.rm=T)
mean(lig2$growth, na.rm=T)
mean(lig3$growth, na.rm=T)
mean(lig4$growth, na.rm=T)
mean(lig5$growth, na.rm=T)
t.test(log10(hum1$growth),log10(lig1$growth))
t.test(log10(hum2$growth),log10(lig2$growth))
t.test(log10(hum3$growth),log10(lig3$growth))
t.test(log10(hum4$growth),log10(lig4$growth))
t.test(log10(hum5$growth),log10(lig5$growth))

hum1 = out2[out2$fire_day ==1 & out2$eco1==11,]
hum2 = out2[out2$fire_day ==2 & out2$eco1==11,]
hum3 = out2[out2$fire_day ==3 & out2$eco1==11,]
hum4 = out2[out2$fire_day ==4 & out2$eco1==11,]
hum5 = out2[out2$fire_day ==5 & out2$eco1==11,]
lig1 = out1[out1$fire_day ==1 & out1$eco1==11,]
lig2 = out1[out1$fire_day ==2 & out1$eco1==11,]
lig3 = out1[out1$fire_day ==3 & out1$eco1==11,]
lig4 = out1[out1$fire_day ==4 & out1$eco1==11,]
lig5 = out1[out1$fire_day ==5 & out1$eco1==11,]
mean(hum1$growth)
mean(hum2$growth)
mean(hum3$growth)
mean(hum4$growth)
mean(hum5$growth)
mean(lig1$growth, na.rm=T)
mean(lig2$growth, na.rm=T)
mean(lig3$growth, na.rm=T)
mean(lig4$growth, na.rm=T)
mean(lig5$growth, na.rm=T)
#t.test(log10(hum1$growth),log10(lig1$growth))
#t.test(log10(hum2$growth),log10(lig2$growth))
t.test(log10(hum3$growth),log10(lig3$growth))
t.test(log10(hum4$growth),log10(lig4$growth))
t.test(log10(hum5$growth),log10(lig5$growth))

out1 = subset(res,cause == 1 )   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,cause !=1 & cause != 14 )

mean(out1$ros_km,na.rm=T)
[1] 0.8405408
mean(out2$ros_km,na.rm=T)
[1] 1.82709
hist.a =hist(out1$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
hist.b =hist(out2$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

fr = barplot(fg, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab="Number of fire days",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5,30.5,33.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")


out1 = subset(res,cause == 1 & (eco1 == 6 | eco1 == 7))   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,cause !=1 & cause != 14 & (eco1 == 6 | eco1 == 7))

hist.a =hist(out1$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
hist.b =hist(out2$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

fr = barplot(fg, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab="Number of fire days",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5,30.5,33.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")


out1 = subset(res,cause == 1 & eco1 == 11)   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,cause !=1 & cause != 14 & eco1 == 11)

hist.a =hist(out1$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
hist.b =hist(out2$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

fr = barplot(fg, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab="Number of fire days",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5,30.5,33.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")


############ autumn northern california
out1 = subset(res,cause == 1 & (eco1 == 6 | eco1 == 7) & (month > 9))   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,cause !=1 & cause != 14 & (eco1 == 6 | eco1 == 7) & (month > 9))

hist.a =hist(out1$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
hist.b =hist(out2$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

fr = barplot(fg, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab="Number of fire days",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5,30.5,33.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")


############ autumn mediterean california
out1 = subset(res,cause == 1 & eco1 == 11 & (month > 9))   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,cause !=1 & cause != 14 & eco1 == 11 & (month > 9))

hist.a =hist(out1$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
hist.b =hist(out2$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

fr = barplot(fg, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab="Number of fire days",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5,30.5,33.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")


############ summer northern california
out1 = subset(res,cause == 1 & (eco1 == 6 | eco1 == 7) & (month > 5 & month<10))   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,cause !=1 & cause != 14 & (eco1 == 6 | eco1 == 7) & (month > 5 & month<10))

hist.a =hist(out1$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
hist.b =hist(out2$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

fr = barplot(fg, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab="Number of fire days",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5,30.5,33.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")


############ summer mediterean california
out1 = subset(res,cause == 1 & eco1 == 11  & (month > 5 & month<10))   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,cause !=1 & cause != 14 & eco1 == 11  & (month > 5 & month<10))

hist.a =hist(out1$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
hist.b =hist(out2$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

fr = barplot(fg, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab="Number of fire days",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5,30.5,33.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")

out1 = subset(res,cause == 1 )   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,cause !=1 & cause != 14 )


mean(out1$ros_km,na.rm=T)
[1] 0.8405408
mean(out2$ros_km,na.rm=T)
[1] 1.82709
#0,0.25,0.5,1,2,3,5,7,10,20,30
hist.a =hist(out1$ros_km,breaks =c(0,0.5,1,2,3,5,7,10,20,30),plot=F)
hist.b =hist(out2$ros_km,breaks =c(0,0.5,1,2,3,5,7,10,20,30),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)
dens = rbind((hist.a$counts/(sum(hist.a$counts)))*100,(hist.b$counts/(sum(hist.b$counts)))*100)

fr = barplot(fg, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab="Number of fire days",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")


fr = barplot(dens, beside=TRUE,xlab=expression('Rate-of-Spread (km d'^-1*')'),ylab="% fire days",ylim=c(0,60),cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/figure2_freq_v1.pdf", width = 6, height = 5)
fr = barplot(dens, beside=TRUE,xlab=expression('Rate-of-Spread (km d'^-1*')'),ylab="Fire days (%)",ylim=c(0,60),cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")
dev.off() 
quartz_off_screen 
                3 

###burnt area by rate-of-spread


data_test = data_s1[data_s1$max_land == 1,]
data_test1 = data_s1[data_s1$L1CODE == 6 |data_s1$L1CODE == 7 ,]
data_test2 = data_s1[data_s1$L1CODE == 11,]

data_test1 = data_s1[data_s1$human == 1 & (data_s1$L1CODE == 6 |data_s1$L1CODE == 7),]
data_test2 = data_s1[data_s1$human == 2 & (data_s1$L1CODE == 6 |data_s1$L1CODE == 7),]

data_test1 = data_s1[data_s1$human == 1 ,]
data_test2 = data_s1[data_s1$human == 2 ,]

fast = data_test1[data_test1$ros_km > 3.64,]
sum(fast$size,na.rm=T)/sum(data_test1$size,na.rm=T)
[1] 0.4992649
sum((data_test1$size*data_test1$ros_km),na.rm=T)/sum(data_test1$size,na.rm=T)
[1] 5.610449
fast = data_test2[data_test2$ros_km > 2.2,]
sum(fast$size,na.rm=T)/sum(data_test2$size,na.rm=T)
[1] 0.499989
sum((data_test2$size*data_test2$ros_km),na.rm=T)/sum(data_test2$size,na.rm=T)
[1] 3.532566
fast = data_s1[data_s1$ros_km > 1,]
fast_hum = fast[fast$human == 1,]

print("% BA and % of fire days fast fires > 1km/day")
[1] "% BA and % of fire days fast fires > 1km/day"
sum(fast$size)/sum(data_s1$size)
[1] 0.8359887
length(fast$size)/length(data_s1$size)
[1] 0.3371951
print("% BA  fast fires due to human ignition % of fire days human caused fast fires > 1km/day")
[1] "% BA  fast fires due to human ignition % of fire days human caused fast fires > 1km/day"
sum(fast_hum$size, na.rm=T)/sum(fast$size)
[1] 0.4634998
length(fast_hum$size)/length(fast$size)
[1] 0.4195298
all_min1 = data_s1[data_s1$nr_day != 1,] # remove first fire spread day from statistics

quan = quantile(data_s1$ros_km,0.9)
fast = data_s1[data_s1$ros_km > quan,]
slow = data_s1[data_s1$ros_km < quan,]
fast_hum = fast[fast$human == 1,]

print("fastest 10% fires cause xxx% of BA")
[1] "fastest 10% fires cause xxx% of BA"
sum(fast$size)/sum(all_min1$size)
[1] 0.5493523
length(fast$size)/length(all_min1$size)
[1] 0.1
print("mean tree mortality weighted by BA and just mean")
[1] "mean tree mortality weighted by BA and just mean"
sum((data_s1$mean_BA_red*data_s1$size))/(sum(data_s1$size))
[1] 49.03327
mean(data_s1$mean_BA_red)
[1] 25.64874
print("% BA due to human fires amoung fastest 10% fire days")
[1] "% BA due to human fires amoung fastest 10% fire days"
sum(fast_hum$size, na.rm=T)/sum(fast$size)
[1] 0.5143879
print("% fire number due to human fires amoung fastest 10% fire days")
[1] "% fire number due to human fires amoung fastest 10% fire days"
length(fast_hum$size)/length(fast$size)
[1] 0.5060976
print("% tree mortality fast fires weighthed and not")
[1] "% tree mortality fast fires weighthed and not"
sum((fast$mean_BA_red*fast$size))/(sum(fast$size))
[1] 60.02651
mean(fast$mean_BA_red)
[1] 52.79125
print("% tree mortality slow fires weighthed and not")
[1] "% tree mortality slow fires weighthed and not"
sum((slow$mean_BA_red*slow$size))/(sum(slow$size))
[1] 35.63221
mean(slow$mean_BA_red)
[1] 22.63291
print("tree mortality <0.5km and >2km")
[1] "tree mortality <0.5km and >2km"
fast1 = data_s1[data_s1$ros_km > 2,]
slow1 = data_s1[data_s1$ros_km < 0.5,]
mean(fast1$mean_BA_red,omit.na=T )
[1] 48.30526
mean(slow1$mean_BA_red,omit.na=T )
[1] 15.29343
# plot BA per rate of spread 
out1 = subset(data_s1,cause == 1 )   #1=lightning; 14=unknown; 7=arson
out2 = subset(data_s1,cause !=1 & cause != 14 )

breaks =c(0,0.5,1,2,3,5,7,10,20,30)

tt=0
pp=0
for (i in 1:9){

  kr = out1[out1$ros_km >= breaks[i] & out1$ros_km < breaks[i+1],]
  kp = out2[out2$ros_km >= breaks[i] & out2$ros_km < breaks[i+1],]

tt[i]=sum(kr$size, na.rm=T)/1000000
pp[i]= sum(kp$size, na.rm=T)/1000000
}
sum(tt)
[1] 6781.34
sum(pp)
[1] 5612.935
fg = rbind(tt,pp)

par(mar=c(4, 5, 2,0.1))
fr = barplot(fg, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab=expression('burnt area (km'^2*')'),ylim=c(0,2000),cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels= breaks ,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")


tiff("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/BA_per_ros_hist.tif", width = 6, height = 5, units = 'in', res = 300)
par(mar=c(4, 5, 2,0.1))
fr = barplot(fg, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab=expression('burnt area (km'^2*')'),ylim=c(0,2000),cex.lab=1.4,cex.axis = 1.3)
#axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels=hist.a$breaks,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels= breaks ,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")
dev.off() 
quartz_off_screen 
                3 
fg1=rbind((tt/sum(tt))*100,(pp/sum(pp))*100)

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/BA_per_ros_hist_percentage.pdf", width = 6, height = 5)
#par(mar=c(1, 1, 1,1))
fr = barplot(fg1, beside=TRUE,xlab=expression('Rate-of-Spread (km d'^-1*')'),ylab= "Burned area (%)",ylim=c(0,30),cex.lab=1.4,cex.axis = 1.3)
#axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels=hist.a$breaks,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels= breaks ,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")
dev.off() 
quartz_off_screen 
                3 

20 fastest fires


res1 = res[res$ros_km > 10 & !is.na(res$ros_km),]
Error in res$ros_km : object of type 'closure' is not subsettable

are ROS the same for light & human under the same conditions

out1 = subset(res,cause == 1 )   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,cause !=1 & cause != 14 )

plot(out1$vpd,log(out1$ros_km))
points(out2$vpd,log(out2$ros_km),col="red")

summary(lm(out1$vpd~log(out1$ros_km+1),na.omit=T))
summary(lm(out2$vpd~log(out2$ros_km+1)))

analysis of the first day

load data


daily_res=read.table("/Users/stijnhantson/Documents/projects/VIIRS_ros/all_ignitions_V3.txt",header=T)

res=as.data.frame(daily_res)

res$bi =as.numeric(as.character(res$bi))
res$erc =as.numeric(as.character(res$erc))
res$etr =as.numeric(as.character(res$etr))
res$fm100 =as.numeric(as.character(res$fm100))
res$fm1000 =as.numeric(as.character(res$fm1000))
res$pet =as.numeric(as.character(res$pet))
res$pr =as.numeric(as.character(res$pr))
res$rmax =as.numeric(as.character(res$rmax))
res$rmin =as.numeric(as.character(res$rmin))
res$th =as.numeric(as.character(res$th))
res$tmmn =as.numeric(as.character(res$tmmn))
res$tmmx =as.numeric(as.character(res$tmmx))
res$vpd =as.numeric(as.character(res$vpd))
res$ws =as.numeric(as.character(res$ws))
res$vs =as.numeric(as.character(res$vs))
res$total_area =as.numeric(as.character(res$total_area))
res$max_land =as.numeric(as.character(res$max_land))
res$mean_land =as.numeric(as.character(res$mean_land))

res$biomass =as.numeric(as.character(res$biomass))

res = res[-1,]
res$human[res$cause ==1] =1
res$human[res$cause !=1 & res$cause !=14] =0

analysis


out1 = res[res$cause !=1 & res$cause != 14,] 
out2 = res[res$cause ==1,] 
length(out1$bi)
[1] 1196
length(out2$bi)
[1] 486
out1 = subset(res,(eco1 == 6 |eco1 == 7) & res$cause !=1 & res$cause != 14)   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,(eco1 == 6 |eco1 == 7) & res$cause ==1)   #1=lightning; 14=unknown; 7=arson
length(out1$bi)
[1] 335
length(out2$bi)
[1] 375
out1 = subset(res,eco1 == 11& res$cause !=1 & res$cause != 14)
out2 = subset(res,eco1 == 11 & res$cause ==1)
length(out1$bi)
[1] 784
length(out2$bi)
[1] 45
out1 = subset(res,(eco1 == 6 |eco1 == 7) & res$cause !=1 & res$cause != 14 & res$mont > 5 & res$mont < 10 )   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,(eco1 == 6 |eco1 == 7) & res$cause ==1& res$mont > 5 & res$mont < 10)   #1=lightning; 14=unknown; 7=arson
length(out1$bi)
[1] 248
length(out2$bi)
[1] 352
out1 = subset(res,(eco1 == 6 |eco1 == 7) & res$cause !=1 & res$cause != 14 & res$mont < 6 & res$mont > 9 )   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,(eco1 == 6 |eco1 == 7) & res$cause ==1& res$mont < 6 & res$mont > 9)   #1=lightning; 14=unknown; 7=arson

length(out1$bi)
[1] 0
length(out2$bi)
[1] 0
t.test(out1$bi,out2$bi)
Error in t.test.default(out1$bi, out2$bi) : not enough 'x' observations

analysis of MTBS temporal trend

library(raster)
library(rgeos)
mtbs_dir="/Users/stijnhantson/Documents/data/MTBS/DATA/"
med=shapefile("/Users/stijnhantson/Documents/data/veg_california/med_cal.shp")
north=shapefile("/Users/stijnhantson/Documents/data/veg_california/north_cal.shp")

year=1984
k=0
dnbr_all_med=0
rdnbr_all_med=0
dnbr_all_nor=0
rdnbr_all_nor=0

len_dnbr_med=0
len_rdnbr_med=0
len_dnbr_nor=0
len_rdnbr_nor=0
for (year in 1984:2017){
  print(year)
  k=k+1
mtbs_dir_year = paste(mtbs_dir,year,"/",sep="")
bndy_list = list.files(mtbs_dir_year, pattern = "burn_bndy.shp$", recursive = T, full.names=T)
shapefile_list <- lapply(bndy_list, shapefile)
fires <- do.call(rbind, shapefile_list)
fires=gUnaryUnion(fires)
fire_north = intersect(fires,north)
fire_med = intersect(fires,med)

dnbr = raster(paste(mtbs_dir,year,"_dnbr.tif",sep=""))
  dnbr[dnbr < -2000] <- NA
  rdnbr = dnbr = raster(paste(mtbs_dir,year,"_rdnbr.tif",sep=""))
  rdnbr[rdnbr < -2000] <- NA
  
dnbr_ext_med = extract(dnbr,fire_med)
dnbr_ext_nor = extract(dnbr,fire_north)

rdnbr_ext_med = extract(rdnbr,fire_med)
rdnbr_ext_nor = extract(rdnbr,fire_north)

dnbr_all_med[k]=mean(unlist(dnbr_ext_med),na.rm=T)
rdnbr_all_med[k]=mean(unlist(rdnbr_ext_med),na.rm=T)
dnbr_all_nor[k]=mean(unlist(dnbr_ext_nor),na.rm=T)
rdnbr_all_nor[k]=mean(unlist(rdnbr_ext_nor),na.rm=T)

len_dnbr_med[k] = length(unlist(dnbr_ext_med))
len_rdnbr_med[k] = length(unlist(rdnbr_ext_med))
len_dnbr_nor[k] = length(unlist(dnbr_ext_nor))
len_rdnbr_nor[k] = length(unlist(rdnbr_ext_nor))
removeTmpFiles(0)
gc()
}

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the Preview button or press Cmd+Shift+K to preview the HTML file).

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike Knit, Preview does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.

```

---
title: "R Notebook"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 


Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Cmd+Option+I*.




```{r}
library(raster)
size_dat=read.table("/Users/stijnhantson/Documents/projects/VIIRS_ros/fire_growth_5days_v4.txt", header=T, row.names=NULL)

size_dat=as.data.frame(size_dat)
size_dat=size_dat[-1,]
size_dat=size_dat[,-1]
colnames(size_dat)=c("firename","year","cause","size1","size2","size3","size4","size5","final_firesize","mean_precip1","mean_precip2","mean_precip3","mean_precip4","mean_precip5","mean_tmax1","mean_tmax2","mean_tmax3","mean_tmax4","mean_tmax5","mean_tmean1","mean_tmean2","mean_tmean3","mean_tmean4","mean_tmean5","mean_vpdmax1","mean_vpdmax2","mean_vpdmax3","mean_vpdmax4","mean_vpdmax5","mean_windspeed1","mean_windspeed2","mean_windspeed3","mean_windspeed4","mean_windspeed5","landcover","ecosystem","biomass","elevation")

size_dat2 <- data.frame(lapply(size_dat, function(x) as.numeric(as.character(x))))
size_dat2$human = 0
size_dat2$human[size_dat2$cause !=1 & size_dat2$cause !=14 & size_dat2$cause !=17]=1
size_dat2$human[size_dat2$cause ==1 ]=2

pro1 =size_dat2[which(size_dat2$human == 2 & size_dat2$landcover == 1 ), ]
pro2 =size_dat2[which(size_dat2$human == 1 & size_dat2$landcover == 1 ), ]

t.test(pro1$size1,pro2$size1)
t.test(pro1$size2,pro2$size2)
t.test(pro1$size3,pro2$size3)
t.test(pro1$size4,pro2$size4)
t.test(pro1$size5,pro2$size5)


pro =size_dat2[which(size_dat2$human == 1 & size_dat2$landcover == 1), ]
length(pro$year)
boxplot(pro$size1,pro$size2,pro$size3,pro$size4,pro$size5,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,500), cex.lab=1.4,cex.axis = 1.3)

pro =size_dat2[which(size_dat2$human == 2 & size_dat2$landcover == 1), ]
length(pro$year)
boxplot(pro$size1,pro$size2,pro$size3,pro$size4,pro$size5,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,500), cex.lab=1.4,cex.axis = 1.3)

pro =size_dat2[which(size_dat2$human == 1 & size_dat2$landcover == 2), ]
length(pro$year)
boxplot(pro$size1,pro$size2,pro$size3,pro$size4,pro$size5,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,500), cex.lab=1.4,cex.axis = 1.3)

pro =size_dat2[which(size_dat2$human == 2 & size_dat2$landcover == 2), ]
length(pro$year)
boxplot(pro$size1,pro$size2,pro$size3,pro$size4,pro$size5,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,500), cex.lab=1.4,cex.axis = 1.3)
length(!is.na(pro$size5))



pro =size_dat2[which(size_dat2$human == 1 & size_dat2$ecosystem == 6), ]
length(pro$year)
boxplot(pro$size1,pro$size2,pro$size3,pro$size4,pro$size5,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,500), cex.lab=1.4,cex.axis = 1.3)

pro =size_dat2[which(size_dat2$human == 2& size_dat2$ecosystem == 6), ]
length(pro$year)
boxplot(pro$size1,pro$size2,pro$size3,pro$size4,pro$size5,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,500), cex.lab=1.4,cex.axis = 1.3)


```





############ plot fire size map QGIS #####################################

```{r}
library(data.table)
DT= data.table(res)
fire_size = DT[ , .SD[which.min(growth_km)], by = firename]

fire_size = fire_size[,c()]
spread_list =list.files(viirs_dir, pattern = "_daily.shp$", recursive = TRUE, full.names=T)
i=1
 l=shapefile(spread_list[i])
for (i in 2:length(spread_list)){
  p=shapefile(spread_list[i])
  l <- rbind(l, p, makeUniqueIDs = TRUE) 
}
 l$human = 0
l$human[l$CAUSE !=1 & l$CAUSE !=14 & l$CAUSE !=17]=1
l$human[l$CAUSE ==1 ]=2

     writeOGR(l, "/Users/stijnhantson/Documents/projects/VIIRS_ros/", layer= "all_fires", driver="ESRI Shapefile", overwrite_layer = T)

```





##### extract number and size statistics from frap   ################
```{r}
library(raster)

dr =shapefile("/Users/stijnhantson/Documents/projects/VIIRS_ros/frap_subset.shp")
dr1 =shapefile("/Users/stijnhantson/Documents/data/FRAP/fire18_1.shp")
dr1$YEAR_=as.numeric(as.character(dr1$YEAR_))
dr1$Shape_Area=as.numeric(as.character(dr1$Shape_Area))
dr1=dr1[!is.na(dr1$YEAR_), ]
dr1=dr1[dr1$YEAR_>2011,]

sum(dr$Shape_Area)/sum(dr1$Shape_Area)
sum(na.omit(dr$GIS_ACRES))/sum(na.omit(dr1$GIS_ACRES))


dr$sqkm = dr$GIS_ACRES * 0.0040468564224
max(dr$sqkm)
dr_h = dr[dr$CAUSE !=1 & dr$CAUSE !=14,]
dr_l = dr[dr$CAUSE == 1,]
dr_o = dr[dr$CAUSE == 14,]

print("mean fire size for human and lightning fires")
mean(dr_h$GIS_ACRES)* 0.0040468564224
mean(dr_l$GIS_ACRES)* 0.0040468564224

quantile(dr_h$GIS_ACRES)* 0.0040468564224
quantile(dr_l$GIS_ACRES)* 0.0040468564224

sum(dr_h$GIS_ACRES)* 0.0040468564224
sum(dr_l$GIS_ACRES)* 0.0040468564224
sum(dr_o$GIS_ACRES)* 0.0040468564224
sum(dr$GIS_ACRES)* 0.0040468564224

hist.a =hist(dr$sqkm,breaks =c(0,10,25,50,100,250,1000,2000),plot=F)

fr = barplot(hist.a$counts, beside=TRUE,xlab=expression('Fire size (km'^2*')'),ylab="Number of fires",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.1,1.3,2.5,3.7,4.9,6.1,7.3,8.5),labels=c(0,10,25,50,100,250,1000,2000),cex.axis = 1.3)

pdf(file="/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/all_firesize.pdf",width=7,height=6)
fr = barplot(hist.a$counts, beside=TRUE,xlab=expression('Fire size (km'^2*')'),ylab="Number of fires",ylim=c(0,50),cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.1,1.3,2.5,3.7,4.9,6.1,7.3,8.5),labels=c(0,10,25,50,100,250,1000,2000),cex.axis = 1.3)
dev.off()

hist.a =hist(dr_h$sqkm,breaks =c(0,10,25,50,100,250,1000,2000),plot=F)

pdf(file="/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/human_firesize.pdf",width=7,height=6)
fr = barplot(hist.a$counts, beside=TRUE,xlab=expression('Fire size (km'^2*')'),ylab="Number of fires",ylim=c(0,50),cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.1,1.3,2.5,3.7,4.9,6.1,7.3,8.5),labels=c(0,10,25,50,100,250,1000,2000),cex.axis = 1.3)
dev.off()

hist.a =hist(dr_l$sqkm,breaks =c(0,10,25,50,100,250,1000,2000),plot=F)

pdf(file="/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/lightning_firesize.pdf",width=7,height=6)
fr = barplot(hist.a$counts, beside=TRUE,xlab=expression('Fire size (km'^2*')'),ylab="Number of fires", ylim=c(0,50),cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.1,1.3,2.5,3.7,4.9,6.1,7.3,8.5),labels=c(0,10,25,50,100,250,1000,2000),cex.axis = 1.3)
dev.off()

```



```{r}
library(raster)

dr1 =shapefile("/Users/stijnhantson/Documents/data/FRAP/fire18_1.shp")
dr1$YEAR_=as.numeric(as.character(dr1$YEAR_))
dr1$Shape_Area=as.numeric(as.character(dr1$Shape_Area))
dr1$ALARM_DATE = as.Date(dr1$ALARM_DATE )
dr1=dr1[!is.na(dr1$YEAR_), ]
dr1=dr1[dr1$YEAR_>2011,]


dr2=dr1[dr1$YEAR_ == 2013,]
plot(table(dr2$ALARM_DATE), xlim=c(as.Date("2013/01/01"),as.Date("2013/12/31")))
table(factor(dr2$ALARM_DATE, levels = as.Date("2013/01/01"):as.Date("2013/12/31")))

```

########### human lightning unknown ratio through time ###################
```{r}


library(raster)
library(rgdal)
library(rgeos)
library(sf)
frap=readOGR("/Users/stijnhantson/Documents/data/FRAP/fire19_1.shp")

shape = shapefile("/Users/stijnhantson/Documents/data/veg_california/ca_eco_l3/ca_eco_l3.shp")
shape = spTransform(shape,crs(frap))


frap = st_make_valid(st_read("/Users/stijnhantson/Documents/data/FRAP/fire19_1.shp"))
types <- vapply(sf::st_geometry(frap), function(x) {
  class(x)[2]
}, "")
polys <- frap[ grepl("*POLYGON", types), ]

frap=sf:::as_Spatial(polys)
frap=gBuffer(frap, byid=T,  width=0.0)
eco_inter=intersect(frap,shape)

frap$YEAR_ =as.numeric(as.character(frap$YEAR_))

# frap2012 = subset(frap,YEAR_ >2011)
#  frap2012 = subset(frap2012,GIS_ACRES >50000)
#length(frap2012)
# frap2012$FIRE_NAME
north = eco_inter[eco_inter$NA_L1CODE== 6 | eco_inter$NA_L1CODE == 7, ]
south= eco_inter[eco_inter$NA_L1CODE== 11, ]

frap=north
frap$GIS_ACRES = frap$GIS_ACRES*0.00404685642
frap_h = subset(frap, CAUSE != 14 & CAUSE != 1)
frap_l = subset(frap, CAUSE == 1)
frap_u = subset(frap, CAUSE == 14 )

hum=0
lig=0
unk=0
pr=0
for (pa in 1980:2019){
  pr=pr+1
  frap_h1 = subset(frap_h,YEAR_ == pa)
  frap_l1 = subset(frap_l,YEAR_ == pa)
  frap_u1 = subset(frap_u,YEAR_ == pa)
  
  hum[pr] = sum(frap_h1$GIS_ACRES,na.rm=T)
  lig[pr] =  sum(frap_l1$GIS_ACRES,na.rm=T)
  unk[pr] = sum(frap_u1$GIS_ACRES,na.rm=T)
  
}

hum5=0
lig5=0
unk5=0
end1=0
k=1
for (k in 1:8){
  st = k*5-4
  end1 = k*5
  hum5[k] = sum(hum[st:end1])
  lig5[k] = sum(lig[st:end1])  
  unk5[k] = sum(unk[st:end1])
}

unk5[8] = unk5[8]*1.2
hum5[8] = hum5[8]*1.2
lig5[8] = lig5[8]*1.2

tr <- plot(lig5, type = "o", col="orange",ylim=c(0,6000), ylab="Burnt area (km2/yr)", xaxt='n', xlab="Years")
lines(hum5, type = "o",col = "red")
lines(unk5, type = "o",col = "lightgrey")
axis(1, at=c(1,2,3,4,5,6,7,8),labels=c("80-84","85-89","90-94","95-99","00-04","05-09","10-14","15-19"))
legend(x="topleft", legend=c("human","lightning","unknown"),col=c("red","orange","lightgrey"),lty = 1,cex=1.2,pch=1,bty = "n")


plot(c(1980:2019),hum,ylim=c(0,1000000),type="l")
lines(c(1980:2019),lig,col="green")
lines(c(1980:2019),unk,col="red")

hum_frac=(lig)/(hum+lig+unk)
plot(hum_frac)


```




1) frap 
2) only fire growth datasets

- prepare final dataset to open
```{r}

library(raster)
#library(rgdal)
daily_res=read.table("/Users/stijnhantson/Documents/projects/VIIRS_ros/final_dataset_V5.txt",header=T)

res=as.data.frame(daily_res)

res$mean_ros =as.numeric(as.character(res$mean_ros))
res$max_ros =as.numeric(as.character(res$max_ros))
res$median95_ros =as.numeric(as.character(res$median95_ros))
res$bi =as.numeric(as.character(res$bi))
res$erc =as.numeric(as.character(res$erc))
res$etr =as.numeric(as.character(res$etr))
res$fm100 =as.numeric(as.character(res$fm100))
res$fm1000 =as.numeric(as.character(res$fm1000))
res$pet =as.numeric(as.character(res$pet))
res$pr =as.numeric(as.character(res$pr))
res$rmax =as.numeric(as.character(res$rmax))
res$rmin =as.numeric(as.character(res$rmin))
res$th =as.numeric(as.character(res$th))
res$tmmn =as.numeric(as.character(res$tmmn))
res$tmmx =as.numeric(as.character(res$tmmx))
res$vpd =as.numeric(as.character(res$vpd))
#res$ws =as.numeric(as.character(res$ws))
res$vs =as.numeric(as.character(res$vs))
res$growth =as.numeric(as.character(res$growth))
res$total_area =as.numeric(as.character(res$total_area))
res$mean_frp =as.numeric(as.character(res$mean_frp))
res$frp_95 =as.numeric(as.character(res$frp_95))
res$max_land =as.numeric(as.character(res$max_land))
res$mean_land =as.numeric(as.character(res$mean_land))
res$biomass =as.numeric(as.character(res$biomass))
res$year =as.numeric(as.character(res$year))
res$month =as.numeric(as.character(res$month))
res$doy_out =as.numeric(as.character(res$doy_out))

res$mean_dnbr =as.numeric(as.character(res$mean_dnbr))
res$mean_rdnbr =as.numeric(as.character(res$mean_rdnbr))
res$mean_bas =as.numeric(as.character(res$mean_bas))
res$median_dnbr =as.numeric(as.character(res$median_dnbr))
res$median_rdnbr =as.numeric(as.character(res$median_rdnbr))
res$median_bas =as.numeric(as.character(res$median_bas))
res$q95_dnbr =as.numeric(as.character(res$q95_dnbr))
res$q95_rdnbr =as.numeric(as.character(res$q95_rdnbr))
res$q95_bas =as.numeric(as.character(res$q95_bas))


res = res[-1,]
res$per_ba = res$growth/res$total_area
res$growth_km =res$growth/1000000

res$human = 0
res$human[res$cause !=1 & res$cause !=14 & res$cause !=17]=1
res$human[res$cause ==1 ]=2

res$ros_km = (res$median95_ros*24)/1000
res$ros_mean_km = (res$mean_ros*24)/1000

```
########  is there relation between days untill 75% and fire size ##########

```{r}
dr1 =shapefile("/Users/stijnhantson/Documents/data/FRAP/fire18_1.shp")
dr1$YEAR_=as.numeric(as.character(dr1$YEAR_))
dr1$Shape_Area=as.numeric(as.character(dr1$Shape_Area))
dr1=dr1[!is.na(dr1$YEAR_), ]
dr1=dr1[dr1$YEAR_>2011,]

fi = dr1[dr1$OBJECTID==17495,]
dr1=subset(dr1, GIS_ACRES>300)

summary(dr1)
res75 = res[res$per_ba > 0.999,]

peak_day1 = as.data.frame(aggregate(res75$fire_day, by = list(res75$firename,res75$year), min))
p=13
year1=0
first=0
last=0
len_d=0
day75=0
firenam1=0
name_fire=0
size=0
k=0
for (p in 1:(length(peak_day1$Group.1)))
{
  print(p)
  year = peak_day1[p,2]
    firenam = as.character(peak_day1[p,1]) 
  
  fir = dr1[which(dr1$YEAR_==year & dr1$FIRE_NAME==firenam),]
  if (length(fir)>1){
    maxsize = max(fir$GIS_ACRES)
    fir = fir[fir$GIS_ACRES ==  maxsize,]
  }
  
  if (!is.na(fir$ALARM_DATE)  | !is.na(fir$ALARM_DATE)){
    k=k+1
    day75[k] = peak_day1[p,3]
    firenam1[k]=firenam 
  year1[k] = year
  first[k]= fir$ALARM_DATE
  last[k] = fir$CONT_DATE
  len_d[k] = as.numeric(as.Date(fir$CONT_DATE) -as.Date(fir$ALARM_DATE))
 name_fire[k]= fir$OBJECTID
    size[k] = fir$GIS_ACRES
  }
}
plot(len_d,day75, xlab="fire duration FRAP (days)", ylab="fire duration VIIRS (days)")

```




####### plot mean vs max fire rate-of-spread ##################
```{r}
#summary(res)
plot(res$ros_km,res$ros_mean_km, xlab="maximum fire-spread-rate (km/day",ylab="mean fire-spread-rate (km/day)")


pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/mean_vs_max_ros_v1.pdf", width = 7, height = 7)
plot(res$ros_km,res$ros_mean_km, xlim=c(0,25),ylim=c(0,10), xlab="fire rate-of-spread (km/day)",ylab="mean fire rate-of-spread (km/day)", cex.lab=1.3,cex.axis = 1.25)
dev.off()

```



############ difference between human and lightnign fires #################

```{r}
me=0
me1=0
days = c("day1","day2","day3","day4","day5")
days1 = c("1","2","3","4","5")
pro1 = res[res$fire_day == 1 & res$human == 1,]
pro2 = res[res$fire_day == 2 & res$human == 1,]
pro3 = res[res$fire_day == 3 & res$human == 1,]
pro4 = res[res$fire_day == 4 & res$human == 1,]
pro5 = res[res$fire_day == 5 & res$human == 1,]
me[1] =mean(pro1$growth_km,na.omit=T)
me[2] =mean(pro2$growth_km,na.omit=T)
me[3] =mean(pro3$growth_km,na.omit=T)
me[4] =mean(pro4$growth_km,na.omit=T)
me[5] =mean(pro5$growth_km,na.omit=T)

pro1h = res[res$fire_day == 1 & res$human == 2,]
pro2h = res[res$fire_day == 2 & res$human == 2,]
pro3h = res[res$fire_day == 3 & res$human == 2,]
pro4h = res[res$fire_day == 4 & res$human == 2,]
pro5h = res[res$fire_day == 5 & res$human == 2,]
me1[1] =mean(pro1h$growth_km,na.omit=T)
me1[2] =mean(pro2h$growth_km,na.omit=T)
me1[3] =mean(pro3h$growth_km,na.omit=T)
me1[4] =mean(pro4h$growth_km,na.omit=T)
me1[5] =mean(pro5h$growth_km,na.omit=T)

boxplot(pro1$growth_km,pro2$growth_km,pro3$growth_km,pro4$growth_km,pro5$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,250), cex.lab=1.4,cex.axis = 1.3)

boxplot(pro1h$growth_km,pro2h$growth_km,pro3h$growth_km,pro4h$growth_km,pro5h$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,250), cex.lab=1.4,cex.axis = 1.3)

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/figure1_v2.pdf", width = 10, height = 5)
par(mfrow=c(1,2))
par(mar=c(4, 4, 1,0.1))
par(mgp=c(2.3,1,0))
boxplot(pro1$growth_km,pro2$growth_km,pro3$growth_km,pro4$growth_km,pro5$growth_km,names=c("1","2","3","4","5"),xlab="Day since fire start",ylab= expression('Fire size (km'^2*')'),ylim=c(0,200), cex.lab=1.4,cex.axis = 1.3)
text(0.3,195,"a)",pos=4, cex=1.4)
boxplot(pro1h$growth_km,pro2h$growth_km,pro3h$growth_km,pro4h$growth_km,pro5h$growth_km,names=c("1","2","3","4","5"),xlab="Day since fire start",ylab= "",ylim=c(0,200), cex.lab=1.4,cex.axis = 1.3)
text(0.3,195,"b)",pos=4, cex=1.4)
dev.off()

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_figure1_v2.pdf", width = 10, height = 5)
par(mfrow=c(1,2))
par(mar=c(4, 4, 1,0.1))
par(mgp=c(2.3,1,0))
boxplot(pro1$growth_km,pro2$growth_km,pro3$growth_km,pro4$growth_km,pro5$growth_km,names=c("1","2","3","4","5"),xlab="Day since fire start",ylab= expression('Fire size (km'^2*')'),ylim=c(0,700), cex.lab=1.4,cex.axis = 1.3)
text(0.3,690,"a)",pos=4, cex=1.4)
boxplot(pro1h$growth_km,pro2h$growth_km,pro3h$growth_km,pro4h$growth_km,pro5h$growth_km,names=c("1","2","3","4","5"),xlab="Day since fire start",ylab= "",ylim=c(0,700), cex.lab=1.4,cex.axis = 1.3)
text(0.3,690,"b)",pos=4, cex=1.4)
dev.off() 

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig4/all_v3.pdf", width = 4, height = 5)
par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n',cex.axis=1.4, lty = 1, lwd = 2)
axis(side=1, at=c(1:5),labels=days1,cex.axis=1.4)
points(me1,type="o",lty = 2, lwd = 2)
legend(x="topleft", legend=c("human","lightning"),col="black",lty = c(1,2),pch=1,bty = "n",lwd = 2,cex=1.5, pt.cex = 1)

dev.off() 

t.test(log(pro1$growth_km),log(pro1h$growth_km))
t.test(log(pro2$growth_km),log(pro2h$growth_km))
t.test(log(pro3$growth_km),log(pro3h$growth_km))
t.test(log(pro4$growth_km),log(pro4h$growth_km))
t.test(log(pro5$growth_km),log(pro5h$growth_km))


```

##################3 for western cordillera ecoregion  ##################
```{r}
me=0
me1=0
pro1 = res[res$fire_day == 1 & res$human == 1 & (res$eco1 == 6 | res$eco1 == 7),]
pro2 = res[res$fire_day == 2 & res$human == 1 & (res$eco1 == 6 | res$eco1 == 7),]
pro3 = res[res$fire_day == 3 & res$human == 1 & (res$eco1 == 6 | res$eco1 == 7),]
pro4 = res[res$fire_day == 4 & res$human == 1 & (res$eco1 == 6 | res$eco1 == 7),]
pro5 = res[res$fire_day == 5 & res$human == 1 & (res$eco1 == 6 | res$eco1 == 7),]

boxplot(pro1$growth_km,pro2$growth_km,pro3$growth_km,pro4$growth_km,pro5$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,250), cex.lab=1.4,cex.axis = 1.3)

pro1h = res[res$fire_day == 1 & res$human == 2 & (res$eco1 == 6 | res$eco1 == 7),]
pro2h = res[res$fire_day == 2 & res$human == 2 & (res$eco1 == 6 | res$eco1 == 7),]
pro3h = res[res$fire_day == 3 & res$human == 2 & (res$eco1 == 6 | res$eco1 == 7),]
pro4h = res[res$fire_day == 4 & res$human == 2 & (res$eco1 == 6 | res$eco1 == 7),]
pro5h = res[res$fire_day == 5 & res$human == 2 & (res$eco1 == 6 | res$eco1 == 7),]

boxplot(pro1h$growth_km,pro2h$growth_km,pro3h$growth_km,pro4h$growth_km,pro5h$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,250), cex.lab=1.4,cex.axis = 1.3)

me[1] =mean(pro1$growth_km,na.omit=T)
me[2] =mean(pro2$growth_km,na.omit=T)
me[3] =mean(pro3$growth_km,na.omit=T)
me[4] =mean(pro4$growth_km,na.omit=T)
me[5] =mean(pro5$growth_km,na.omit=T)

me1[1] =mean(pro1h$growth_km,na.rm=T)
me1[2] =mean(pro2h$growth_km,na.rm=T)
me1[3] =mean(pro3h$growth_km,na.rm=T)
me1[4] =mean(pro4h$growth_km,na.rm=T)
me1[5] =mean(pro5h$growth_km,na.rm=T)


par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n', lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days)
points(me1,type="o",lty = 2, lwd = 2)

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig4/north_v2.pdf", width = 4, height = 5)
par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n', lty = 1, lwd = 2,cex.lab=1.2,cex.axis=1.4)
axis(side=1, at=c(1:5),labels=days1,cex.axis=1.4)
points(me1,type="o",lty = 2, lwd = 2)
dev.off() 


t.test(log(pro1$growth_km),log(pro1h$growth_km))
t.test(log(pro2$growth_km),log(pro2h$growth_km))
t.test(log(pro3$growth_km),log(pro3h$growth_km))
t.test(log(pro4$growth_km),log(pro4h$growth_km))
t.test(log(pro5$growth_km),log(pro5h$growth_km))

mean(pro1$growth_km,na.omit=T)
mean(pro1h$growth_km,na.rm=T)

```

################## for mediteranean california  ##################

```{r}
pro1 = res[res$fire_day == 1 & res$human == 1 & (res$eco1 == 11),]
pro2 = res[res$fire_day == 2 & res$human == 1 & (res$eco1 == 11),]
pro3 = res[res$fire_day == 3 & res$human == 1 & (res$eco1 == 11),]
pro4 = res[res$fire_day == 4 & res$human == 1 & (res$eco1 == 11),]
pro5 = res[res$fire_day == 5 & res$human == 1 & (res$eco1 == 11),]

boxplot(pro1$growth_km,pro2$growth_km,pro3$growth_km,pro4$growth_km,pro5$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)

pro1h = res[res$fire_day == 1 & res$human == 2 & (res$eco1 == 11),]
pro2h = res[res$fire_day == 2 & res$human == 2 & (res$eco1 == 11),]
pro3h = res[res$fire_day == 3 & res$human == 2 & (res$eco1 == 11),]
pro4h = res[res$fire_day == 4 & res$human == 2 & (res$eco1 == 11),]
pro5h = res[res$fire_day == 5 & res$human == 2 & (res$eco1 == 11),]

boxplot(pro1h$growth_km,pro2h$growth_km,pro3h$growth_km,pro4h$growth_km,pro5h$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)

me[1] =mean(pro1$growth_km,na.omit=T)
me[2] =mean(pro2$growth_km,na.omit=T)
me[3] =mean(pro3$growth_km,na.omit=T)
me[4] =mean(pro4$growth_km,na.omit=T)
me[5] =mean(pro5$growth_km,na.omit=T)

me1[1] =mean(pro1h$growth_km,na.rm=T)
me1[2] =mean(pro2h$growth_km,na.rm=T)
me1[3] =mean(pro3h$growth_km,na.rm=T)
me1[4] =mean(pro4h$growth_km,na.rm=T)
me1[5] =mean(pro5h$growth_km,na.rm=T)


par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n', lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days)
points(me1,type="o",lty = 2, lwd = 2)

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig4/med_v2.pdf", width = 4, height = 5)
par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="",cex.axis=1.4, xaxt='n', lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days1,cex.axis=1.4)
points(me1,type="o",lty = 2, lwd = 2)
dev.off() 


```
################## for difference in autumn  ##################
```{r}
pro1 = res[res$fire_day == 1 & res$human == 1 & res$month >8,]
pro2 = res[res$fire_day == 2 & res$human == 1 & res$month >8,]
pro3 = res[res$fire_day == 3 & res$human == 1 & res$month >8,]
pro4 = res[res$fire_day == 4 & res$human == 1 & res$month >8,]
pro5 = res[res$fire_day == 5 & res$human == 1 & res$month >8,]

boxplot(pro1$growth_km,pro2$growth_km,pro3$growth_km,pro4$growth_km,pro5$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)

pro1h = res[res$fire_day == 1 & res$human == 2 & res$month >8,]
pro2h = res[res$fire_day == 2 & res$human == 2 & res$month >8,]
pro3h = res[res$fire_day == 3 & res$human == 2 & res$month >8,]
pro4h = res[res$fire_day == 4 & res$human == 2 & res$month >8,]
pro5h = res[res$fire_day == 5 & res$human == 2 & res$month >8,]

boxplot(pro1h$growth_km,pro2h$growth_km,pro3h$growth_km,pro4h$growth_km,pro5h$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)

me[1] =mean(pro1$growth_km,na.omit=T)
me[2] =mean(pro2$growth_km,na.omit=T)
me[3] =mean(pro3$growth_km,na.omit=T)
me[4] =mean(pro4$growth_km,na.omit=T)
me[5] =mean(pro5$growth_km,na.omit=T)

me1[1] =mean(pro1h$growth_km,na.rm=T)
me1[2] =mean(pro2h$growth_km,na.rm=T)
me1[3] =mean(pro3h$growth_km,na.rm=T)
me1[4] =mean(pro4h$growth_km,na.rm=T)
me1[5] =mean(pro5h$growth_km,na.rm=T)


par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n', lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days)
points(me1,type="o",lty = 2, lwd = 2)

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig4/autumn_v2.pdf", width = 4, height = 5)
par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,250),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n',cex.axis=1.4, lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days1,cex.axis=1.4)
points(me1,type="o",lty = 2, lwd = 2)
dev.off() 


```

################## for difference in summer  ##################

```{r}
pro1 = res[res$fire_day == 1 & res$human == 1 & res$month <=8 ,]
pro2 = res[res$fire_day == 2 & res$human == 1 & res$month <=8,]
pro3 = res[res$fire_day == 3 & res$human == 1 & res$month <=8,]
pro4 = res[res$fire_day == 4 & res$human == 1 & res$month <=8,]
pro5 = res[res$fire_day == 5 & res$human == 1 & res$month <=8,]

boxplot(pro1$growth_km,pro2$growth_km,pro3$growth_km,pro4$growth_km,pro5$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)

pro1h = res[res$fire_day == 1 & res$human == 2 & res$month <=8,]
pro2h = res[res$fire_day == 2 & res$human == 2 & res$month <=8,]
pro3h = res[res$fire_day == 3 & res$human == 2 & res$month <=8,]
pro4h = res[res$fire_day == 4 & res$human == 2 & res$month <=8,]
pro5h = res[res$fire_day == 5 & res$human == 2 & res$month <=8,]

boxplot(pro1h$growth_km,pro2h$growth_km,pro3h$growth_km,pro4h$growth_km,pro5h$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)

me[1] =mean(pro1$growth_km,na.omit=T)
me[2] =mean(pro2$growth_km,na.omit=T)
me[3] =mean(pro3$growth_km,na.omit=T)
me[4] =mean(pro4$growth_km,na.omit=T)
me[5] =mean(pro5$growth_km,na.omit=T)

me1[1] =mean(pro1h$growth_km,na.rm=T)
me1[2] =mean(pro2h$growth_km,na.rm=T)
me1[3] =mean(pro3h$growth_km,na.rm=T)
me1[4] =mean(pro4h$growth_km,na.rm=T)
me1[5] =mean(pro5h$growth_km,na.rm=T)


par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n', lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days)
points(me1,type="o",lty = 2, lwd = 2)

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig4/summer_v2.pdf", width = 4, height = 5)
par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n',cex.axis=1.4, lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days1,cex.axis=1.4)
points(me1,type="o",lty = 2, lwd = 2)
dev.off() 

```

################## for difference in summer in western cordillera  ##################

```{r}
pro1 = res[res$fire_day == 1 & res$human == 1 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro2 = res[res$fire_day == 2 & res$human == 1 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro3 = res[res$fire_day == 3 & res$human == 1 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro4 = res[res$fire_day == 4 & res$human == 1 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro5 = res[res$fire_day == 5 & res$human == 1 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 6 | res$eco1 == 7),]

boxplot(pro1$growth_km,pro2$growth_km,pro3$growth_km,pro4$growth_km,pro5$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)

pro1h = res[res$fire_day == 1 & res$human == 2 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro2h = res[res$fire_day == 2 & res$human == 2 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro3h = res[res$fire_day == 3 & res$human == 2 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro4h = res[res$fire_day == 4 & res$human == 2 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro5h = res[res$fire_day == 5 & res$human == 2 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 6 | res$eco1 == 7),]

boxplot(pro1h$growth_km,pro2h$growth_km,pro3h$growth_km,pro4h$growth_km,pro5h$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)

t.test(log(pro1$growth_km),log(pro1h$growth_km))
t.test(log(pro2$growth_km),log(pro2h$growth_km))
t.test(log(pro3$growth_km),log(pro3h$growth_km))
t.test(log(pro4$growth_km),log(pro4h$growth_km))
t.test(log(pro5$growth_km),log(pro5h$growth_km))

me[1] =mean(pro1$growth_km,na.omit=T)
me[2] =mean(pro2$growth_km,na.omit=T)
me[3] =mean(pro3$growth_km,na.omit=T)
me[4] =mean(pro4$growth_km,na.omit=T)
me[5] =mean(pro5$growth_km,na.omit=T)

me1[1] =mean(pro1h$growth_km,na.rm=T)
me1[2] =mean(pro2h$growth_km,na.rm=T)
me1[3] =mean(pro3h$growth_km,na.rm=T)
me1[4] =mean(pro4h$growth_km,na.rm=T)
me1[5] =mean(pro5h$growth_km,na.rm=T)

par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n', lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days)
points(me1,type="o",lty = 2, lwd = 2)

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig4/north_summer_v2.pdf", width = 4, height = 5)
par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="",cex.axis=1.4, xaxt='n', lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days1,cex.axis=1.4)
points(me1,type="o",lty = 2, lwd = 2)
dev.off() 


```

################## for difference in autumn in western cordillera  ##################
```{r}
pro1 = res[res$fire_day == 1 & res$human == 1 & ( res$month >8 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro2 = res[res$fire_day == 2 & res$human == 1 & ( res$month >8 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro3 = res[res$fire_day == 3 & res$human == 1 & ( res$month >8 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro4 = res[res$fire_day == 4 & res$human == 1 & ( res$month >8 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro5 = res[res$fire_day == 5 & res$human == 1 & ( res$month >8 ) & (res$eco1 == 6 | res$eco1 == 7),]

boxplot(pro1$growth_km,pro2$growth_km,pro3$growth_km,pro4$growth_km,pro5$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)

pro1h = res[res$fire_day == 1 & res$human == 2 & ( res$month >8 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro2h = res[res$fire_day == 2 & res$human == 2 & ( res$month >8 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro3h = res[res$fire_day == 3 & res$human == 2 & ( res$month >8 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro4h = res[res$fire_day == 4 & res$human == 2 & (res$month >8 ) & (res$eco1 == 6 | res$eco1 == 7),]
pro5h = res[res$fire_day == 5 & res$human == 2 & ( res$month >8 ) & (res$eco1 == 6 | res$eco1 == 7),]

boxplot(pro1h$growth_km,pro2h$growth_km,pro3h$growth_km,pro4h$growth_km,pro5h$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)

me[1] =mean(pro1$growth_km,na.omit=T)
me[2] =mean(pro2$growth_km,na.omit=T)
me[3] =mean(pro3$growth_km,na.omit=T)
me[4] =mean(pro4$growth_km,na.omit=T)
me[5] =mean(pro5$growth_km,na.omit=T)

me1[1] =mean(pro1h$growth_km,na.rm=T)
me1[2] =mean(pro2h$growth_km,na.rm=T)
me1[3] =mean(pro3h$growth_km,na.rm=T)
me1[4] =mean(pro4h$growth_km,na.rm=T)
me1[5] =mean(pro5h$growth_km,na.rm=T)

par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n', lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days)
points(me1,type="o",lty = 2, lwd = 2)

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig4/north_autumn_v2.pdf", width = 4, height = 5)
par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,250),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n',cex.axis=1.4, lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days1,cex.axis=1.4)
points(me1,type="o",lty = 2, lwd = 2)
dev.off() 


```
################## for difference in summer in meditereanean  ##################

```{r}
pro1 = res[res$fire_day == 1 & res$human == 1 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 11),]
pro2 = res[res$fire_day == 2 & res$human == 1 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 11),]
pro3 = res[res$fire_day == 3 & res$human == 1 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 11),]
pro4 = res[res$fire_day == 4 & res$human == 1 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 11),]
pro5 = res[res$fire_day == 5 & res$human == 1 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 11),]

boxplot(pro1$growth_km,pro2$growth_km,pro3$growth_km,pro4$growth_km,pro5$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)

pro1h = res[res$fire_day == 1 & res$human == 2 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 11),]
pro2h = res[res$fire_day == 2 & res$human == 2 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 11),]
pro3h = res[res$fire_day == 3 & res$human == 2 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 11),]
pro4h = res[res$fire_day == 4 & res$human == 2 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 11),]
pro5h = res[res$fire_day == 5 & res$human == 2 & ( res$month <=8 & res$month >5 ) & (res$eco1 == 11),]

boxplot(pro1h$growth_km,pro2h$growth_km,pro3h$growth_km,pro4h$growth_km,pro5h$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)

#t.test(log(pro1$growth_km),log(pro1h$growth_km))
#t.test(log(pro2$growth_km),log(pro2h$growth_km))
#t.test(log(pro3$growth_km),log(pro3h$growth_km))
#t.test(log(pro4$growth_km),log(pro4h$growth_km))
#t.test(log(pro5$growth_km),log(pro5h$growth_km))

me[1] =mean(pro1$growth_km,na.omit=T)
me[2] =mean(pro2$growth_km,na.omit=T)
me[3] =mean(pro3$growth_km,na.omit=T)
me[4] =mean(pro4$growth_km,na.omit=T)
me[5] =mean(pro5$growth_km,na.omit=T)

me1[1] =mean(pro1h$growth_km,na.rm=T)
me1[2] =mean(pro2h$growth_km,na.rm=T)
me1[3] =mean(pro3h$growth_km,na.rm=T)
me1[4] =mean(pro4h$growth_km,na.rm=T)
me1[5] =mean(pro5h$growth_km,na.rm=T)

par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n', lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days)
points(me1,type="o",lty = 2, lwd = 2)

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig4/med_summer_v2.pdf", width = 4, height = 5)
par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n',cex.axis=1.4, lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days1,cex.axis=1.4)
points(me1,type="o",lty = 2, lwd = 2)
dev.off() 


```

`
################## for difference in autumn in mediteranean  ##################
```{r}
pro1 = res[res$fire_day == 1 & res$human == 1 & ( res$month >8 ) & (res$eco1 == 11),]
pro2 = res[res$fire_day == 2 & res$human == 1 & ( res$month >8 ) & (res$eco1 == 11),]
pro3 = res[res$fire_day == 3 & res$human == 1 & ( res$month >8 ) & (res$eco1 == 11),]
pro4 = res[res$fire_day == 4 & res$human == 1 & ( res$month >8 ) & (res$eco1 == 11),]
pro5 = res[res$fire_day == 5 & res$human == 1 & ( res$month >8 ) & (res$eco1 == 11),]

boxplot(pro1$growth_km,pro2$growth_km,pro3$growth_km,pro4$growth_km,pro5$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)

pro1h = res[res$fire_day == 1 & res$human == 2 & ( res$month >8 ) & (res$eco1 == 11),]
pro2h = res[res$fire_day == 2 & res$human == 2 & ( res$month >8 ) & (res$eco1 == 11),]
pro3h = res[res$fire_day == 3 & res$human == 2 & ( res$month >8 ) & (res$eco1 == 11),]
pro4h = res[res$fire_day == 4 & res$human == 2 & ( res$month >8 ) & (res$eco1 == 11),]
pro5h = res[res$fire_day == 5 & res$human == 2 & ( res$month >8 ) & (res$eco1 == 11),]

boxplot(pro1h$growth_km,pro2h$growth_km,pro3h$growth_km,pro4h$growth_km,pro5h$growth_km,names=c("day1","day2","day3","day4","day5"),xlab="",ylab="fire size (km2)",ylim=c(0,300), cex.lab=1.4,cex.axis = 1.3)

me[1] =mean(pro1$growth_km,na.omit=T)
me[2] =mean(pro2$growth_km,na.omit=T)
me[3] =mean(pro3$growth_km,na.omit=T)
me[4] =mean(pro4$growth_km,na.omit=T)
me[5] =mean(pro5$growth_km,na.omit=T)

me1[1] =mean(pro1h$growth_km,na.rm=T)
me1[2] =mean(pro2h$growth_km,na.rm=T)
me1[3] =mean(pro3h$growth_km,na.rm=T)
me1[4] =mean(pro4h$growth_km,na.rm=T)
me1[5] =mean(pro5h$growth_km,na.rm=T)

par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,150),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n', lty = 1, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days)
points(me1,type="o",lty = 2, lwd = 2)

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig4/med_autumn_v2.pdf", width = 4, height = 5)
par(mgp=c(2.3,1,0))  
plot(me,type="o", ylim=c(0,400),ylab=expression("fire size (km"^2*")"),xlab="", xaxt='n', lty = 1,cex.axis=1.4, lwd = 2,cex.lab=1.2)
axis(side=1, at=c(1:5),labels=days1,cex.axis=1.4)
points(me1,type="o",lty = 2, lwd = 2)
dev.off() 


```

############ how many days does it take to reach 75% burnt area #################

```{r}
res75 = res[res$per_ba > 0.75,]
#peak_day = as.data.frame(aggregate(res75$fire_day, by = list(res75$firename,res75$cause), min))
#peak_day=subset(peak_day,x < 55)
#hi = hist(peak_day$x,prob =F, breaks= c(0:54), xlim=c(0,55), ylab="number of fires", xlab="days", cex.lab=1.4,cex.axis=1.3)

out1 = subset(res75,cause == 1 )   #1=lightning; 14=unknown; 7=arson
out2 = subset(res75,cause !=1 & cause != 14 )


peak_day1 = as.data.frame(aggregate(out1$fire_day, by = list(out1$firename), min))
peak_day2 = as.data.frame(aggregate(out2$fire_day, by = list(out2$firename), min))
peak=as.data.frame(aggregate(res75$fire_day, by = list(res75$firename), min))
quantile(peak_day1$x,0.50,type=3) 
quantile(peak_day2$x,0.50,type=3) 

peak_day1=subset(peak_day1,x < 56)
peak_day2=subset(peak_day2,x < 56)
hist.a =hist(peak_day1$x,breaks =c(0:55),plot=F)
hist.b =hist(peak_day2$x,breaks =c(0:55),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

fr = barplot(fg,xlab="Days after ignition",ylab="Number of fires",cex.lab=1.4,cex.axis = 1.3, xlim=c(1,65), ylim=c(0,30))
axis(1,c(0.7,5.5,11.5,17.5,23.5,29.5,35.5,41.5,47.5,53.5,59.5,65.5),labels=c(1,5,10,15,20,25,30,35,40,45,50,55),cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")

pdf(file="/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/time_to_reach75.pdf",width=7,height=5)
fr = barplot(fg,xlab="Days after ignition",ylab="Number of fires",cex.lab=1.4,cex.axis = 1.3, xlim=c(1,65), ylim=c(0,30))
axis(1,c(0.7,5.5,11.5,17.5,23.5,29.5,35.5,41.5,47.5,53.5,59.5,65.5),labels=c(1,5,10,15,20,25,30,35,40,45,50,55),cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")
dev.off()

########  mediteranean  ###########
out1 = subset(res75,cause == 1 & res75$eco1 == 11)   #1=lightning; 14=unknown; 7=arson
out2 = subset(res75,cause !=1 & cause != 14 & res75$eco1 == 11)
peak_day1 = as.data.frame(aggregate(out1$fire_day, by = list(out1$firename), min))
peak_day2 = as.data.frame(aggregate(out2$fire_day, by = list(out2$firename), min))

peak_day1=subset(peak_day1,x < 56)
peak_day2=subset(peak_day2,x < 56)
hist.a =hist(peak_day1$x,breaks =c(0:55),plot=F)
hist.b =hist(peak_day2$x,breaks =c(0:55),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

pdf(file="/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig5_time75/med.pdf",width=7,height=5)
fr = barplot(fg,xlab="Days after ignition",ylab="Number of fires",cex.lab=1.4,cex.axis = 1.3, xlim=c(1,65), ylim=c(0,15))
axis(1,c(0.7,5.5,11.5,17.5,23.5,29.5,35.5,41.5,47.5,53.5,59.5,65.5),labels=c(1,5,10,15,20,25,30,35,40,45,50,55),cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")
dev.off()

########  north cal  ###########
out1 = subset(res75,cause == 1  & (res75$eco1 == 6 | res75$eco1 == 7))   #1=lightning; 14=unknown; 7=arson
out2 = subset(res75,cause !=1 & cause != 14  & (res75$eco1 == 6 | res75$eco1 == 7))
peak_day1 = as.data.frame(aggregate(out1$fire_day, by = list(out1$firename), min))
peak_day2 = as.data.frame(aggregate(out2$fire_day, by = list(out2$firename), min))

peak_day1=subset(peak_day1,x < 56)
peak_day2=subset(peak_day2,x < 56)
hist.a =hist(peak_day1$x,breaks =c(0:55),plot=F)
hist.b =hist(peak_day2$x,breaks =c(0:55),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

pdf(file="/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig5_time75/north.pdf",width=7,height=5)
fr = barplot(fg,xlab="Days after ignition",ylab="Number of fires",cex.lab=1.4,cex.axis = 1.3, xlim=c(1,65), ylim=c(0,15))
axis(1,c(0.7,5.5,11.5,17.5,23.5,29.5,35.5,41.5,47.5,53.5,59.5,65.5),labels=c(1,5,10,15,20,25,30,35,40,45,50,55),cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")
dev.off()

########  mediteranean  SUMMER ###########
out1 = subset(res75,cause == 1 & res75$eco1 == 11 & ( res75$month <=8 & res75$month >5 ))   #1=lightning; 14=unknown; 7=arson
out2 = subset(res75,cause !=1 & cause != 14 & res75$eco1 == 11 & ( res75$month <=8 & res75$month >5 ))
peak_day1 = as.data.frame(aggregate(out1$fire_day, by = list(out1$firename), min))
peak_day2 = as.data.frame(aggregate(out2$fire_day, by = list(out2$firename), min))

peak_day1=subset(peak_day1,x < 56)
peak_day2=subset(peak_day2,x < 56)
hist.a =hist(peak_day1$x,breaks =c(0:55),plot=F)
hist.b =hist(peak_day2$x,breaks =c(0:55),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

pdf(file="/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig5_time75/med_summer.pdf",width=7,height=5)
fr = barplot(fg,xlab="Days after ignition",ylab="Number of fires",cex.lab=1.4,cex.axis = 1.3, xlim=c(1,65), ylim=c(0,10))
axis(1,c(0.7,5.5,11.5,17.5,23.5,29.5,35.5,41.5,47.5,53.5,59.5,65.5),labels=c(1,5,10,15,20,25,30,35,40,45,50,55),cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")
dev.off()

########  north cal  summer ###########
out1 = subset(res75,cause == 1  & (res75$eco1 == 6 | res75$eco1 == 7)& ( res75$month <=8 & res75$month >5 ))   #1=lightning; 14=unknown; 7=arson
out2 = subset(res75,cause !=1 & cause != 14  & (res75$eco1 == 6 | res75$eco1 == 7)& ( res75$month <=8 & res75$month >5 ))
peak_day1 = as.data.frame(aggregate(out1$fire_day, by = list(out1$firename), min))
peak_day2 = as.data.frame(aggregate(out2$fire_day, by = list(out2$firename), min))

peak_day1=subset(peak_day1,x < 56)
peak_day2=subset(peak_day2,x < 56)
hist.a =hist(peak_day1$x,breaks =c(0:55),plot=F)
hist.b =hist(peak_day2$x,breaks =c(0:55),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

pdf(file="/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig5_time75/north_summer.pdf",width=7,height=5)
fr = barplot(fg,xlab="Days after ignition",ylab="Number of fires",cex.lab=1.4,cex.axis = 1.3, xlim=c(1,65), ylim=c(0,12))
axis(1,c(0.7,5.5,11.5,17.5,23.5,29.5,35.5,41.5,47.5,53.5,59.5,65.5),labels=c(1,5,10,15,20,25,30,35,40,45,50,55),cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")
dev.off()

########  mediteranean  autumn###########
out1 = subset(res75,cause == 1 & res75$eco1 == 11 & ( res75$month >8))   #1=lightning; 14=unknown; 7=arson
out2 = subset(res75,cause !=1 & cause != 14 & res75$eco1 == 11 & ( res75$month >8 ))
#peak_day1 = as.data.frame(aggregate(out1$fire_day, by = list(out1$firename), min))
peak_day2 = as.data.frame(aggregate(out2$fire_day, by = list(out2$firename), min))

#peak_day1=subset(peak_day1,x < 56)
peak_day2=subset(peak_day2,x < 56)
#hist.a =hist(peak_day1$x,breaks =c(0:55),plot=F)
hist.b =hist(peak_day2$x,breaks =c(0:55),plot=F)
fg = rbind(0,hist.b$counts)

pdf(file="/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig5_time75/med_autumn.pdf",width=7,height=5)
fr = barplot(fg,xlab="Days after ignition",ylab="Number of fires",cex.lab=1.4,cex.axis = 1.3, xlim=c(1,65), ylim=c(0,5))
axis(1,c(0.7,5.5,11.5,17.5,23.5,29.5,35.5,41.5,47.5,53.5,59.5,65.5),labels=c(1,5,10,15,20,25,30,35,40,45,50,55),cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")
dev.off()

########  north cal  autumn ###########
out1 = subset(res75,cause == 1  & (res75$eco1 == 6 | res75$eco1 == 7)& ( res75$month >8))   #1=lightning; 14=unknown; 7=arson
out2 = subset(res75,cause !=1 & cause != 14  & (res75$eco1 == 6 | res75$eco1 == 7)& ( res75$month >8 ))
peak_day1 = as.data.frame(aggregate(out1$fire_day, by = list(out1$firename), min))
peak_day2 = as.data.frame(aggregate(out2$fire_day, by = list(out2$firename), min))

peak_day1=subset(peak_day1,x < 56)
peak_day2=subset(peak_day2,x < 56)
hist.a =hist(peak_day1$x,breaks =c(0:55),plot=F)
hist.b =hist(peak_day2$x,breaks =c(0:55),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

pdf(file="/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/sup_fig5_time75/north_autumn.pdf",width=7,height=5)
fr = barplot(fg,xlab="Days after ignition",ylab="Number of fires",cex.lab=1.4,cex.axis = 1.3, xlim=c(1,65), ylim=c(0,5))
axis(1,c(0.7,5.5,11.5,17.5,23.5,29.5,35.5,41.5,47.5,53.5,59.5,65.5),labels=c(1,5,10,15,20,25,30,35,40,45,50,55),cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")
dev.off()
```


```{r}

res=res[res$ros_km>0,]
res_f = res[res$max_land == 1,]
res_p = res[res$max_land != 1,]

summary(lm(log(res$mean_frp)~log(res$ros_km)))

#just show the plot here
plot(res_f$mean_frp~res_f$ros_km,log="xy",xlim=c(0.005,30),ylim=c(0.1,180),xaxt="n",ylab="mean FRP (MW)",xlab="Rate-of-Spread (km/day)", cex.lab=1.4,cex.axis = 1.3,col="darkgreen")

marks=c(0.01,0.1,1,10)
marks1=c(0.1,0.5,5,50)


pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/fig_FRP_ros_v3.pdf", width = 5, height = 5)
plot(res_f$mean_frp~res_f$ros_km,log="xy",xlim=c(0.005,30),ylim=c(0.1,180),xaxt="n",yaxt="n",ylab="mean FRP (MW)",xlab=expression('Rate-of-Spread (km d'^-1*')'), cex.lab=1.4,cex.axis = 1.3,col="darkgreen")
points(res_p$mean_frp~res_p$ros_km,col="orange")
axis(1,at=marks,labels=marks,cex.axis=1.4 )
axis(2,at=marks1,labels=marks1,cex.axis=1.4 )
legend( x="topleft",legend=c("Forest","Grass & shrub"),col=c("darkgreen","orange"),cex=1.2,pch=1,bty = "n")
dev.off()



```

######## FRP vs tree mortality
```{r}
summary(res)
res=res[res$ros_km>0,]

data_test1 = res[res$human == 1 & res$max_land <1.5,]
data_test2 = res1[res$human == 2 & res$max_land <1.5,]

summary(lm(log(res$mean_frp)~res$mean_bas))

#just show the plot here
plot(res$mean_bas~res$mean_frp,log="x",ylim=c(0,100),xlim=c(0.1,200),ylab="mean tree mortality",xlab="mean FRP (MW)", cex.lab=1.4,cex.axis = 1.3,col="darkgreen")

marks=c(0.1,1,10,100)
marks1=c(0,25,50,75,100)


pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/fig_FRP_mortality_v1.pdf", width = 5, height = 5)
plot(res$mean_bas~res$mean_frp,log="x",ylim=c(0,100),xlim=c(0.1,200),xaxt="n",yaxt="n",ylab="Tree mortality (%)",xlab="mean FRP (MW)", cex.lab=1.4,cex.axis = 1.3,col="darkgreen")
axis(1,at=marks,labels=marks,cex.axis=1.4 )
axis(2,at=marks1,labels=marks1,cex.axis=1.4 )
dev.off()

```




```{r}
data_s=read.table("/Users/stijnhantson/Documents/projects/VIIRS_ros/daily_mean_ros_dNBR_V6.txt",row.names=NULL)
rownames(data_s) <- c()

data_s1 = as.data.frame(data_s[,2:19])


names(data_s1) = c("lon","lat","fire","nr_day","max_land","mean_land","elevation","biomass","mean_ros","ros95","mean_dnbr","dnbr95","mean_rdnbr","rdnbr95","mean_BA_red","BA_red95","cause","size")
length(data_s1$lon)
data_s1$mean_ros =as.numeric(as.character(data_s1$mean_ros))
data_s1$ros95 =as.numeric(as.character(data_s1$ros95))
data_s1$mean_dnbr =as.numeric(as.character(data_s1$mean_dnbr))
data_s1$dnbr95 =as.numeric(as.character(data_s1$dnbr95))
data_s1$mean_rdnbr =as.numeric(as.character(data_s1$mean_rdnbr))
data_s1$rdnbr95 =as.numeric(as.character(data_s1$rdnbr95))
data_s1$lon =as.numeric(as.character(data_s1$lon))
data_s1$lat =as.numeric(as.character(data_s1$lat))

data_s1$mean_land[is.na(data_s1$mean_land)]=data_s1$max_land[is.na(data_s1$mean_land)] #mean landcover gives NA when only one landcover is present
data_s1$mean_land =as.numeric(as.character(data_s1$mean_land))
data_s1$mean_BA_red =as.numeric(as.character(data_s1$mean_BA_red))
data_s1$BA_red95 =as.numeric(as.character(data_s1$BA_red95))
data_s1$biomass =as.numeric(as.character(data_s1$biomass))
data_s1$elevation =as.numeric(as.character(data_s1$elevation))
data_s1$cause =as.numeric(as.character(data_s1$cause))
data_s1$size =as.numeric(as.character(data_s1$size))


data_s1=na.omit(data_s1)

shape = shapefile("/Users/stijnhantson/Documents/data/veg_california/ca_eco_l3/ca_eco_l3.shp")
pts <- SpatialPoints(data_s1[,c("lon","lat")],P4S.latlon)
shape = spTransform(shape,P4S.latlon)
eco = over(pts, shape)
data_s1$L1CODE = eco$NA_L1CODE
data_s1$L3name = eco$US_L3NAME
data_s1$L1CODE =as.numeric(as.character(data_s1$L1CODE))

#data_s1$log_ros = log10(data_s1$mean_ros)
#data_s1$log_ros95 = log10(data_s1$ros95)
#data_s1 = data_s1[data_s1$mean_ros >0,]
data_s1$human[data_s1$cause !=1 & data_s1$cause !=14 & data_s1$cause !=17]=1
data_s1$human[data_s1$cause ==1 ]=2

data_s1$ros_km = (data_s1$ros95 *24)/1000
data_test = data_s1[data_s1$max_land == 1,]
data_test1 = data_s1[data_s1$L1CODE == 6 |data_s1$L1CODE == 7 ,]
data_test2 = data_s1[data_s1$L1CODE == 11,]

data_test1 = data_s1[data_s1$human == 1 & (data_s1$L1CODE == 6 |data_s1$L1CODE == 7),]
data_test2 = data_s1[data_s1$human == 2 & (data_s1$L1CODE == 6 |data_s1$L1CODE == 7),]

data_test1 = data_s1[data_s1$human == 1 ,]
data_test2 = data_s1[data_s1$human == 2 ,]

data_test1=na.omit(data_test1)
data_test2=na.omit(data_test2)



plot(data_s1$ros_km, data_s1$mean_BA_red,log="x",xlab=expression('Rate-of-Spread (km d'^-1*')'),ylab="Tree mortality (%)",xlim=c(0.005,30),xaxt="n",cex.axis=1.4 ,cex.lab=1.4,cex=0.8, col="black")
#axis(1,at=marks,labels=marks,cex.axis=1.4 )
points(data_test2$ros_km, data_test2$mean_BA_red,cex=0.8, col="darkgrey")
points(data_test1$ros_km, data_test1$mean_BA_red,cex=0.8, col="orange")


marks=c(0.01,0.1,1,10)
tiff("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/fig_basa_ros_all_v3.tif", width = 5, height = 5, units = 'in', res = 300)
plot(data_s1$ros_km, data_s1$mean_BA_red,log="x",xlab=expression('Rate-of-Spread (km d'^-1*')'),ylab="Tree mortality (%)",xlim=c(0.005,30),xaxt="n",cex.axis=1.4 ,cex.lab=1.4,cex=0.8, col="black")
axis(1,at=marks,labels=marks,cex.axis=1.4 )
points(data_test2$ros_km, data_test2$mean_BA_red,cex=0.8, col="darkgrey")
points(data_test1$ros_km, data_test1$mean_BA_red,cex=0.8, col="orange")
lines(lowess(data_s1$ros_km, data_s1$mean_BA_red, f=0.41),col="black", lwd=3)
lines(lowess(data_test1$ros_km, data_test1$mean_BA_red, f=0.41),col="darkgoldenrod3", lwd=3)
lines(lowess(data_test2$ros_km, data_test2$mean_BA_red, f=0.41),col="gray40", lwd=3)
legend("topleft",legend=c("all","human","lightning"),col = c("black","darkgoldenrod3", "gray40"),lty=1, bty="n",lwd = 3, cex=1)
dev.off()



data_test1 = data_s1[data_s1$human == 1 & data_s1$max_land <1.5 & data_s1$ros_km>0 ,]
data_test2 = data_s1[data_s1$human == 2 & data_s1$max_land <1.5 & data_s1$ros_km>0,]
data_forest = data_s1[data_s1$max_land <1.5 & data_s1$ros_km>0,]

data_test1=na.omit(data_test1)
data_test2=na.omit(data_test2)
data_forest=na.omit(data_forest)

summary(lm(data_forest$mean_BA_red~(log10(data_forest$ros_km)+I((log10(data_forest$ros_km))^2))))
summary(log10(data_forest$ros_km))
data_s2 = data_s1[data_s1$ros_km <10,]
data_forest_10 = data_s2[data_s2$max_land <1.5,]
data_test1_10 = data_s2[data_s2$human == 1 & data_s2$max_land <1.5,]
data_test2_10 = data_s2[data_s2$human == 2 & data_s2$max_land <1.5,]
data_test1_10=na.omit(data_test1_10)
data_test2_10=na.omit(data_test2_10)

marks=c(0.01,0.1,1,10)
pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/fig_basa_ros_forest_v7.pdf", width = 5, height = 5)
plot(data_forest$ros_km, data_forest$mean_BA_red,log="x",xlab=expression('Rate-of-Spread (km d'^-1*')'),ylab="Tree mortality (%)",xlim=c(0.005,30),xaxt="n",cex.axis=1.4 ,cex.lab=1.4,cex=0.8, col="black")
axis(1,at=marks,labels=marks,cex.axis=1.4 )
points(data_test2$ros_km, data_test2$mean_BA_red,cex=0.8, col="darkgrey")
points(data_test1$ros_km, data_test1$mean_BA_red,cex=0.8, col="orange")
lines(lowess(data_forest_10$ros_km, data_forest_10$mean_BA_red, f=0.41),col="black", lwd=3)
lines(lowess(data_test1_10$ros_km, data_test1_10$mean_BA_red, f=0.41),col="darkgoldenrod3", lwd=3)
lines(lowess(data_test2_10$ros_km, data_test2_10$mean_BA_red, f=0.41),col="gray40", lwd=3)
legend("topleft",legend=c("all","human","lightning"),col = c("black","darkgoldenrod3", "gray40"),lty=1, bty="n",lwd = 3, cex=1)
dev.off()

marks=c(0.01,0.1,1,10)
pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/fig_basa_ros_forest_v5.pdf", width = 5, height = 5)
plot(data_forest$ros_km, data_forest$mean_BA_red,log="x",xlab=expression('Rate-of-Spread (km d'^-1*')'),ylab="Tree mortality (%)",xlim=c(0.005,30),xaxt="n",cex.axis=1.4 ,cex.lab=1.4,cex=0.8, col="black")
axis(1,at=marks,labels=marks,cex.axis=1.4 )
points(data_test2$ros_km, data_test2$mean_BA_red,cex=0.8, col="darkgrey")
points(data_test1$ros_km, data_test1$mean_BA_red,cex=0.8, col="orange")
lines(lowess(data_forest_10$ros_km, data_forest_10$mean_BA_red, f=0.41),col="black", lwd=3)
lines(lowess(data_test1_10$ros_km, data_test1_10$mean_BA_red, f=0.41),col="darkgoldenrod3", lwd=3)
lines(lowess(data_test2_10$ros_km, data_test2_10$mean_BA_red, f=0.41),col="gray40", lwd=3)
legend("topleft",legend=c("all","human","lightning"),col = c("black","darkgoldenrod3", "gray40"),lty=1, bty="n",lwd = 3, cex=1)
dev.off()

marks=c(0.01,0.1,1,10)
tiff("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/fig_basa_ros_forest_v6.tif", width = 5, height = 5, units = 'in', res = 300)
plot(data_forest$ros_km, data_forest$mean_BA_red,log="x",xlab=expression('Rate-of-Spread (km d'^-1*')'),ylab="Tree mortality (%)",xlim=c(0.1,15),xaxt="n",cex.axis=1.4 ,cex.lab=1.4,cex=0.8, col="black")
axis(1,at=marks,labels=marks,cex.axis=1.4 )
points(data_test2$ros_km, data_test2$mean_BA_red,cex=0.8, col="darkgrey")
points(data_test1$ros_km, data_test1$mean_BA_red,cex=0.8, col="orange")
lines(lowess(data_forest_10$ros_km, data_forest_10$mean_BA_red, f=0.41),col="black", lwd=3)
lines(lowess(data_test1_10$ros_km, data_test1_10$mean_BA_red, f=0.41),col="darkgoldenrod3", lwd=3)
lines(lowess(data_test2_10$ros_km, data_test2_10$mean_BA_red, f=0.41),col="gray40", lwd=3)
legend("topleft",legend=c("all","human","lightning"),col = c("black","darkgoldenrod3", "gray40"),lty=1, bty="n",lwd = 3, cex=1)
dev.off()


plot(data_forest$ros_km, data_forest$mean_BA_red,log="x",xlab=expression('Rate-of-Spread (km d'^-1*')'),ylab="Tree mortality (%)",xlim=c(0.1,15),xaxt="n",cex.axis=1.4 ,cex.lab=1.4,cex=0.8, col="black")
axis(1,at=marks,labels=marks,cex.axis=1.4 )
points(data_test2$ros_km, data_test2$mean_BA_red,cex=0.8, col="darkgrey")
points(data_test1$ros_km, data_test1$mean_BA_red,cex=0.8, col="orange")
lines(lowess(data_forest_10$ros_km, data_forest_10$mean_BA_red, f=0.41),col="black", lwd=3)
lines(lowess(data_test1_10$ros_km, data_test1_10$mean_BA_red, f=0.41),col="darkgoldenrod3", lwd=3)
lines(lowess(data_test2_10$ros_km, data_test2_10$mean_BA_red, f=0.41),col="gray40", lwd=3)
legend("topleft",legend=c("all","human","lightning"),col = c("black","darkgoldenrod3", "gray40"),lty=1, bty="n",lwd = 3, cex=1)

data_test = data_s1[data_s1$max_land == 1,]

marks=c(0.01,0.1,1,10)
pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/fig_dNBR_ros_all_v2.pdf", width = 5, height = 5)
plot(data_s1$ros_km, data_s1$mean_dnbr,log="x",xlab=expression('Rate-of-Spread (km d'^-1*')'),ylab="dNBR",xlim=c(0.005,30),xaxt="n",cex.axis=1.4 ,cex.lab=1.4,cex=0.8, ylim=c(0,600))
axis(1,at=marks,labels=marks,cex.axis=1.4 )
#lines(lowess(data_test$ros95, data_test$mean_BA_red, f=0.41),col="black", lwd=3)
dev.off()

marks=c(0.01,0.1,1,10)
pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/fig_rdNBR_ros_all_v2.pdf", width = 5, height = 5)
plot(data_s1$ros_km, data_s1$mean_rdnbr,log="x",xlab=expression('Rate-of-Spread (km d'^-1*')'),ylab="rdNBR",xlim=c(0.005,30),xaxt="n",cex.axis=1.4 ,cex.lab=1.4,cex=0.8, ylim=c(0,600))
axis(1,at=marks,labels=marks,cex.axis=1.4 )
#lines(lowess(data_test$ros95, data_test$mean_BA_red, f=0.41),col="black", lwd=3)
dev.off()

test=data_s1[data_s1$ros_km>0,]
summary(lm(log10(test$ros_km)~ test$mean_rdnbr+I(test$mean_rdnbr^2)))
summary(lm(log10(test$ros_km)~ test$mean_dnbr))


marks=c(0.01,0.1,1,10)
pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/fig_dNBR_ros_north_v2.pdf", width = 5, height = 5)
plot(data_test1$ros_km, data_test1$mean_dnbr,log="x",xlab=expression('Rate-of-Spread (km d'^-1*')'),ylab="dNBR",xlim=c(0.005,30),xaxt="n",cex.axis=1.4 ,cex.lab=1.4,cex=0.8, ylim=c(0,600))
axis(1,at=marks,labels=marks,cex.axis=1.4 )
#lines(lowess(data_test$ros95, data_test$mean_BA_red, f=0.41),col="black", lwd=3)
dev.off()

marks=c(0.01,0.1,1,10)
pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/fig_dNBR_ros_south_v2.pdf", width = 5, height = 5)
plot(data_test2$ros_km, data_test2$mean_dnbr,log="x",xlab=expression('Rate-of-Spread (km d'^-1*')'),ylab="dNBR",xlim=c(0.005,30),xaxt="n",cex.axis=1.4 ,cex.lab=1.4,cex=0.8, ylim=c(0,600))
axis(1,at=marks,labels=marks,cex.axis=1.4 )
#lines(lowess(data_test$ros95, data_test$mean_BA_red, f=0.41),col="black", lwd=3)
dev.off()



```



########  difference in fire size for first 5 days across california and both ecosystems 

```{r}
 
res$ros1 = res$max_ros+1


out1 = subset(res,cause == 1 )   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,cause !=1 & cause != 14 )

hum1 = out2[out2$fire_day ==1,]
hum2 = out2[out2$fire_day ==2,]
hum3 = out2[out2$fire_day ==3,]
hum4 = out2[out2$fire_day ==4,]
hum5 = out2[out2$fire_day ==5,]
lig1 = out1[out1$fire_day ==1,]
lig2 = out1[out1$fire_day ==2,]
lig3 = out1[out1$fire_day ==3,]
lig4 = out1[out1$fire_day ==4,]
lig5 = out1[out1$fire_day ==5,]
mean(hum1$growth)
mean(hum2$growth)
mean(hum3$growth)
mean(hum4$growth)
mean(hum5$growth)
mean(lig1$growth)
mean(lig2$growth)
mean(lig3$growth)
mean(lig4$growth)
mean(lig5$growth)
t.test(log10(hum1$growth),log10(lig1$growth))
t.test(log10(hum2$growth),log10(lig2$growth))
t.test(log10(hum3$growth),log10(lig3$growth))
t.test(log10(hum4$growth),log10(lig4$growth))
t.test(log10(hum5$growth),log10(lig5$growth))

hum1 = out2[out2$fire_day ==1 & out2$eco1==6,]
hum2 = out2[out2$fire_day ==2 & out2$eco1==6,]
hum3 = out2[out2$fire_day ==3 & out2$eco1==6,]
hum4 = out2[out2$fire_day ==4 & out2$eco1==6,]
hum5 = out2[out2$fire_day ==5 & out2$eco1==6,]
lig1 = out1[out1$fire_day ==1 & out1$eco1==6,]
lig2 = out1[out1$fire_day ==2 & out1$eco1==6,]
lig3 = out1[out1$fire_day ==3 & out1$eco1==6,]
lig4 = out1[out1$fire_day ==4 & out1$eco1==6,]
lig5 = out1[out1$fire_day ==5 & out1$eco1==6,]
mean(hum1$growth)
mean(hum2$growth)
mean(hum3$growth)
mean(hum4$growth)
mean(hum5$growth)
mean(lig1$growth, na.rm=T)
mean(lig2$growth, na.rm=T)
mean(lig3$growth, na.rm=T)
mean(lig4$growth, na.rm=T)
mean(lig5$growth, na.rm=T)
t.test(log10(hum1$growth),log10(lig1$growth))
t.test(log10(hum2$growth),log10(lig2$growth))
t.test(log10(hum3$growth),log10(lig3$growth))
t.test(log10(hum4$growth),log10(lig4$growth))
t.test(log10(hum5$growth),log10(lig5$growth))

hum1 = out2[out2$fire_day ==1 & out2$eco1==11,]
hum2 = out2[out2$fire_day ==2 & out2$eco1==11,]
hum3 = out2[out2$fire_day ==3 & out2$eco1==11,]
hum4 = out2[out2$fire_day ==4 & out2$eco1==11,]
hum5 = out2[out2$fire_day ==5 & out2$eco1==11,]
lig1 = out1[out1$fire_day ==1 & out1$eco1==11,]
lig2 = out1[out1$fire_day ==2 & out1$eco1==11,]
lig3 = out1[out1$fire_day ==3 & out1$eco1==11,]
lig4 = out1[out1$fire_day ==4 & out1$eco1==11,]
lig5 = out1[out1$fire_day ==5 & out1$eco1==11,]
mean(hum1$growth)
mean(hum2$growth)
mean(hum3$growth)
mean(hum4$growth)
mean(hum5$growth)
mean(lig1$growth, na.rm=T)
mean(lig2$growth, na.rm=T)
mean(lig3$growth, na.rm=T)
mean(lig4$growth, na.rm=T)
mean(lig5$growth, na.rm=T)
#t.test(log10(hum1$growth),log10(lig1$growth))
#t.test(log10(hum2$growth),log10(lig2$growth))
t.test(log10(hum3$growth),log10(lig3$growth))
t.test(log10(hum4$growth),log10(lig4$growth))
t.test(log10(hum5$growth),log10(lig5$growth))


```

```{r}

out1 = subset(res,cause == 1 )   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,cause !=1 & cause != 14 )

mean(out1$ros_km,na.rm=T)
mean(out2$ros_km,na.rm=T)

hist.a =hist(out1$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
hist.b =hist(out2$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

fr = barplot(fg, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab="Number of fire days",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5,30.5,33.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")

out1 = subset(res,cause == 1 & (eco1 == 6 | eco1 == 7))   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,cause !=1 & cause != 14 & (eco1 == 6 | eco1 == 7))

hist.a =hist(out1$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
hist.b =hist(out2$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

fr = barplot(fg, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab="Number of fire days",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5,30.5,33.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")

out1 = subset(res,cause == 1 & eco1 == 11)   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,cause !=1 & cause != 14 & eco1 == 11)

hist.a =hist(out1$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
hist.b =hist(out2$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

fr = barplot(fg, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab="Number of fire days",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5,30.5,33.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")

############ autumn northern california
out1 = subset(res,cause == 1 & (eco1 == 6 | eco1 == 7) & (month > 9))   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,cause !=1 & cause != 14 & (eco1 == 6 | eco1 == 7) & (month > 9))

hist.a =hist(out1$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
hist.b =hist(out2$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

fr = barplot(fg, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab="Number of fire days",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5,30.5,33.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")

############ autumn mediterean california
out1 = subset(res,cause == 1 & eco1 == 11 & (month > 9))   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,cause !=1 & cause != 14 & eco1 == 11 & (month > 9))

hist.a =hist(out1$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
hist.b =hist(out2$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

fr = barplot(fg, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab="Number of fire days",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5,30.5,33.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")

############ summer northern california
out1 = subset(res,cause == 1 & (eco1 == 6 | eco1 == 7) & (month > 5 & month<10))   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,cause !=1 & cause != 14 & (eco1 == 6 | eco1 == 7) & (month > 5 & month<10))

hist.a =hist(out1$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
hist.b =hist(out2$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

fr = barplot(fg, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab="Number of fire days",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5,30.5,33.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")

############ summer mediterean california
out1 = subset(res,cause == 1 & eco1 == 11  & (month > 5 & month<10))   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,cause !=1 & cause != 14 & eco1 == 11  & (month > 5 & month<10))

hist.a =hist(out1$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
hist.b =hist(out2$ros_km,breaks =c(0,0.01,0.05,0.1,0.25,0.5,1,2,5,10,20,30),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)

fr = barplot(fg, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab="Number of fire days",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5,30.5,33.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")

```

```{r}

out1 = subset(res,cause == 1 )   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,cause !=1 & cause != 14 )


mean(out1$ros_km,na.rm=T)
mean(out2$ros_km,na.rm=T)
#0,0.25,0.5,1,2,3,5,7,10,20,30
hist.a =hist(out1$ros_km,breaks =c(0,0.5,1,2,3,5,7,10,20,30),plot=F)
hist.b =hist(out2$ros_km,breaks =c(0,0.5,1,2,3,5,7,10,20,30),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)
dens = rbind((hist.a$counts/(sum(hist.a$counts)))*100,(hist.b$counts/(sum(hist.b$counts)))*100)

fr = barplot(fg, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab="Number of fire days",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")

fr = barplot(dens, beside=TRUE,xlab=expression('Rate-of-Spread (km d'^-1*')'),ylab="% fire days",ylim=c(0,60),cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/figure2_freq_v1.pdf", width = 6, height = 5)
fr = barplot(dens, beside=TRUE,xlab=expression('Rate-of-Spread (km d'^-1*')'),ylab="Fire days (%)",ylim=c(0,60),cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")
dev.off() 


############ autumn northern california
out1 = subset(res,cause == 1 & (eco1 == 6 | eco1 == 7) & (month > 9))   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,cause !=1 & cause != 14 & (eco1 == 6 | eco1 == 7) & (month > 9))

hist.a =hist(out1$ros_km,breaks =c(0,0.5,1,2,3,5,7,10,20,30),plot=F)
hist.b =hist(out2$ros_km,breaks =c(0,0.5,1,2,3,5,7,10,20,30),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)
dens = rbind((hist.a$counts/(sum(hist.a$counts)))*100,(hist.b$counts/(sum(hist.b$counts)))*100)

fr = barplot(fg, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab="Number of fire days",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")

fr = barplot(dens, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab="% fire days",ylim=c(0,80),cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")

tiff("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/S4_freq_autumn_north_v1.tif", width = 6, height = 5, units = 'in', res = 300)
fr = barplot(dens, beside=TRUE,xlab=expression('Rate-of-Spread (km d'^-1*')'),ylab="% fire days",ylim=c(0,80),cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")
dev.off() 

############ autumn mediterean california
out1 = subset(res,cause == 1 & eco1 == 11 & (month > 9))   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,cause !=1 & cause != 14 & eco1 == 11 & (month > 9))

hist.a =hist(out1$ros_km,breaks =c(0,0.5,1,2,3,5,7,10,20,30),plot=F)
hist.b =hist(out2$ros_km,breaks =c(0,0.5,1,2,3,5,7,10,20,30),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)
dens = rbind((hist.a$counts/(sum(hist.a$counts)))*100,(hist.b$counts/(sum(hist.b$counts)))*100)

fr = barplot(fg, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab="Number of fire days",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")

fr = barplot(dens, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab="% fire days",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")

tiff("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/S4_freq_autumn_med_v1.tif", width = 6, height = 5, units = 'in', res = 300)
fr = barplot(dens, beside=TRUE,xlab=expression('Rate-of-Spread (km d'^-1*')'),ylab="% fire days",ylim=c(0,25),cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")
dev.off() 


############ summer northern california
out1 = subset(res,cause == 1 & (eco1 == 6 | eco1 == 7) & (month > 5 & month<10))   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,cause !=1 & cause != 14 & (eco1 == 6 | eco1 == 7) & (month > 5 & month<10))

hist.a =hist(out1$ros_km,breaks =c(0,0.5,1,2,3,5,7,10,20,30),plot=F)
hist.b =hist(out2$ros_km,breaks =c(0,0.5,1,2,3,5,7,10,20,30),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)
dens = rbind((hist.a$counts/(sum(hist.a$counts)))*100,(hist.b$counts/(sum(hist.b$counts)))*100)

fr = barplot(fg, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab="Number of fire days",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")

fr = barplot(dens, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab="% fire days",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")

tiff("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/S4_freq_summer_north_v1.tif", width = 6, height = 5, units = 'in', res = 300)
fr = barplot(dens, beside=TRUE,xlab=expression('Rate-of-Spread (km d'^-1*')'),ylab="% fire days",ylim=c(0,60),cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")
dev.off() 


############ summer mediterean california
out1 = subset(res,cause == 1 & eco1 == 11  & (month > 5 & month<10))   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,cause !=1 & cause != 14 & eco1 == 11  & (month > 5 & month<10))

hist.a =hist(out1$ros_km,breaks =c(0,0.5,1,2,3,5,7,10,20,30),plot=F)
hist.b =hist(out2$ros_km,breaks =c(0,0.5,1,2,3,5,7,10,20,30),plot=F)
fg = rbind(hist.a$counts,hist.b$counts)
dens = rbind((hist.a$counts/(sum(hist.a$counts)))*100,(hist.b$counts/(sum(hist.b$counts)))*100)

fr = barplot(fg, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab="Number of fire days",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")

fr = barplot(dens, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab="% fire days",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")

tiff("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/S4_freq_summer_med_v1.tif", width = 6, height = 5, units = 'in', res = 300)
fr = barplot(dens, beside=TRUE,xlab=expression('Rate-of-Spread (km d'^-1*')'),ylab="% fire days",cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels=hist.a$breaks,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")
dev.off() 


```

###burnt area by rate-of-spread
```{r}

data_test = data_s1[data_s1$max_land == 1,]
data_test1 = data_s1[data_s1$L1CODE == 6 |data_s1$L1CODE == 7 ,]
data_test2 = data_s1[data_s1$L1CODE == 11,]

data_test1 = data_s1[data_s1$human == 1 & (data_s1$L1CODE == 6 |data_s1$L1CODE == 7),]
data_test2 = data_s1[data_s1$human == 2 & (data_s1$L1CODE == 6 |data_s1$L1CODE == 7),]

data_test1 = data_s1[data_s1$human == 1 ,]
data_test2 = data_s1[data_s1$human == 2 ,]

fast = data_test1[data_test1$ros_km > 3.64,]
sum(fast$size,na.rm=T)/sum(data_test1$size,na.rm=T)
sum((data_test1$size*data_test1$ros_km),na.rm=T)/sum(data_test1$size,na.rm=T)

fast = data_test2[data_test2$ros_km > 2.2,]
sum(fast$size,na.rm=T)/sum(data_test2$size,na.rm=T)
sum((data_test2$size*data_test2$ros_km),na.rm=T)/sum(data_test2$size,na.rm=T)

fast = data_s1[data_s1$ros_km > 1,]
fast_hum = fast[fast$human == 1,]

print("% BA and % of fire days fast fires > 1km/day")
sum(fast$size)/sum(data_s1$size)
length(fast$size)/length(data_s1$size)

print("% BA  fast fires due to human ignition % of fire days human caused fast fires > 1km/day")
sum(fast_hum$size, na.rm=T)/sum(fast$size)
length(fast_hum$size)/length(fast$size)

all_min1 = data_s1[data_s1$nr_day != 1,] # remove first fire spread day from statistics

quan = quantile(data_s1$ros_km,0.9)
fast = data_s1[data_s1$ros_km > quan,]
slow = data_s1[data_s1$ros_km < quan,]
fast_hum = fast[fast$human == 1,]

print("fastest 10% fires cause xxx% of BA")
sum(fast$size)/sum(all_min1$size)
length(fast$size)/length(all_min1$size)
print("mean tree mortality weighted by BA and just mean")
sum((data_s1$mean_BA_red*data_s1$size))/(sum(data_s1$size))
mean(data_s1$mean_BA_red)

print("% BA due to human fires amoung fastest 10% fire days")
sum(fast_hum$size, na.rm=T)/sum(fast$size)
print("% fire number due to human fires amoung fastest 10% fire days")
length(fast_hum$size)/length(fast$size)
print("% tree mortality fast fires weighthed and not")
sum((fast$mean_BA_red*fast$size))/(sum(fast$size))
mean(fast$mean_BA_red)
print("% tree mortality slow fires weighthed and not")
sum((slow$mean_BA_red*slow$size))/(sum(slow$size))
mean(slow$mean_BA_red)

print("tree mortality <0.5km and >2km")
fast1 = data_s1[data_s1$ros_km > 2,]
slow1 = data_s1[data_s1$ros_km < 0.5,]
mean(fast1$mean_BA_red,omit.na=T )
mean(slow1$mean_BA_red,omit.na=T )

# plot BA per rate of spread 
out1 = subset(data_s1,cause == 1 )   #1=lightning; 14=unknown; 7=arson
out2 = subset(data_s1,cause !=1 & cause != 14 )

breaks =c(0,0.5,1,2,3,5,7,10,20,30)

tt=0
pp=0
for (i in 1:9){

  kr = out1[out1$ros_km >= breaks[i] & out1$ros_km < breaks[i+1],]
  kp = out2[out2$ros_km >= breaks[i] & out2$ros_km < breaks[i+1],]

tt[i]=sum(kr$size, na.rm=T)/1000000
pp[i]= sum(kp$size, na.rm=T)/1000000
}
sum(tt)
sum(pp)
fg = rbind(tt,pp)

par(mar=c(4, 5, 2,0.1))
fr = barplot(fg, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab=expression('burnt area (km'^2*')'),ylim=c(0,2000),cex.lab=1.4,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels= breaks ,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")


tiff("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/BA_per_ros_hist.tif", width = 6, height = 5, units = 'in', res = 300)
par(mar=c(4, 5, 2,0.1))
fr = barplot(fg, beside=TRUE,xlab="Rate-of-Spread (km/day)",ylab=expression('burnt area (km'^2*')'),ylim=c(0,2000),cex.lab=1.4,cex.axis = 1.3)
#axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels=hist.a$breaks,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels= breaks ,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")
dev.off() 


fg1=rbind((tt/sum(tt))*100,(pp/sum(pp))*100)

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/BA_per_ros_hist_percentage.pdf", width = 6, height = 5)
#par(mar=c(1, 1, 1,1))
fr = barplot(fg1, beside=TRUE,xlab=expression('Rate-of-Spread (km d'^-1*')'),ylab= "Burned area (%)",ylim=c(0,30),cex.lab=1.4,cex.axis = 1.3)
#axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels=hist.a$breaks,cex.axis = 1.3)
axis(1,at=c(0.5,3.5,6.5,9.5,12.5,15.5,18.5,21.5,24.5,27.5),labels= breaks ,cex.axis = 1.3)
legend("topright",legend = c("human","lightning"), fill=c("grey","black"),cex=1.4,bty = "n")
dev.off() 



```



################# 20 fastest fires  #############
```{r}

res1 = res[res$ros_km > 10 & !is.na(res$ros_km),]
length(res1$ros_km)
res1
```


############ are ROS the same for light & human under the same conditions

```{r}
out1 = subset(res,cause == 1 )   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,cause !=1 & cause != 14 )

plot(out1$vpd,log(out1$ros_km))
points(out2$vpd,log(out2$ros_km),col="red")

summary(lm(out1$vpd~log(out1$ros_km+1),na.omit=T))
summary(lm(out2$vpd~log(out2$ros_km+1)))

```





############## analysis of the first day #################

load data
```{r}

daily_res=read.table("/Users/stijnhantson/Documents/projects/VIIRS_ros/all_ignitions_V3.txt",header=T)

res=as.data.frame(daily_res)

res$bi =as.numeric(as.character(res$bi))
res$erc =as.numeric(as.character(res$erc))
res$etr =as.numeric(as.character(res$etr))
res$fm100 =as.numeric(as.character(res$fm100))
res$fm1000 =as.numeric(as.character(res$fm1000))
res$pet =as.numeric(as.character(res$pet))
res$pr =as.numeric(as.character(res$pr))
res$rmax =as.numeric(as.character(res$rmax))
res$rmin =as.numeric(as.character(res$rmin))
res$th =as.numeric(as.character(res$th))
res$tmmn =as.numeric(as.character(res$tmmn))
res$tmmx =as.numeric(as.character(res$tmmx))
res$vpd =as.numeric(as.character(res$vpd))
res$ws =as.numeric(as.character(res$ws))
res$vs =as.numeric(as.character(res$vs))
res$total_area =as.numeric(as.character(res$total_area))
res$max_land =as.numeric(as.character(res$max_land))
res$mean_land =as.numeric(as.character(res$mean_land))

res$biomass =as.numeric(as.character(res$biomass))

res = res[-1,]
res$human[res$cause ==1] =1
res$human[res$cause !=1 & res$cause !=14] =0

```

analysis

```{r}

out1 = res[res$cause !=1 & res$cause != 14,] 
out2 = res[res$cause ==1,] 
length(out1$bi)
length(out2$bi)

out1 = subset(res,(eco1 == 6 |eco1 == 7) & res$cause !=1 & res$cause != 14)   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,(eco1 == 6 |eco1 == 7) & res$cause ==1)   #1=lightning; 14=unknown; 7=arson
length(out1$bi)
length(out2$bi)

out1 = subset(res,eco1 == 11& res$cause !=1 & res$cause != 14)
out2 = subset(res,eco1 == 11 & res$cause ==1)
length(out1$bi)
length(out2$bi)

out1 = subset(res,(eco1 == 6 |eco1 == 7) & res$cause !=1 & res$cause != 14 & res$mont > 5 & res$mont < 10 )   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,(eco1 == 6 |eco1 == 7) & res$cause ==1& res$mont > 5 & res$mont < 10)   #1=lightning; 14=unknown; 7=arson
length(out1$bi)
length(out2$bi)

out1 = subset(res,(eco1 == 6 |eco1 == 7) & res$cause !=1 & res$cause != 14 & res$mont < 6 & res$mont > 9 )   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,(eco1 == 6 |eco1 == 7) & res$cause ==1& res$mont < 6 & res$mont > 9)   #1=lightning; 14=unknown; 7=arson

length(out1$bi)
length(out2$bi)


t.test(out1$bi,out2$bi)
t.test(out1$erc,out2$erc)
t.test(out1$etr,out2$etr)
t.test(out1$fm100,out2$fm100)
t.test(out1$fm1000,out2$fm1000)
t.test(out1$pet,out2$pet)
t.test(out1$pr,out2$pr)
t.test(out1$rmax,out2$rmax)
t.test(out1$rmin,out2$rmin)
t.test(out1$th,out2$th)
t.test(out1$tmmn,out2$tmmn)
t.test(out1$tmmx,out2$tmmx)
t.test(out1$vpd,out2$vpd)
t.test(out1$vs,out2$vs)
t.test(out1$ws,out2$ws)
t.test(out1$biomass,out2$biomass)
t.test(out1$mean_land,out2$mean_land)
t.test(log10(out1$total_area),log10(out2$total_area))



```

```{r}

ta = table(res$human,res$mont)
pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/S9a_v1.pdf", width = 7, height = 6)
ps = barplot(ta, beside=TRUE, ylab="Number of fires",xpd=T,xlab= "Month", xaxt='n',ylim=c(0,300), axis.lty=1,cex.lab = 1.5,cex.axis = 1.4 )
axis(1,at=c(2,5,8,11,14,17,20,23,26,29,32,35), labels =c(1:12),xlim=c(0,36),xpd=F ,cex.lab = 1.4,cex.axis = 1.3)
legend("topright",c("human","lightning"),fill = c("black","grey"), bty="n",cex=1.4)
text(5,285,"a) California",cex=1.7)
dev.off() 

out1 = subset(res,eco1 == 6 |eco1 == 7)   #1=lightning; 14=unknown; 7=arson
out2 = subset(res,eco1 == 11)

ta = table(out1$human,out1$mont)

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/S9b_v1.pdf", width = 7, height = 6)
ps = barplot(ta, beside=TRUE, ylab="Number of fires",xpd=T, xaxt='n',xlab= "Month", ylim=c(0,200), axis.lty=1,cex.lab = 1.5,cex.axis = 1.4 )
axis(1,at=c(2,5,8,11,14,17,20,23,26,29,32,35), labels =c(1:12),xlim=c(0,36),xpd=F,cex.lab = 1.4,cex.axis = 1.3 )
legend("topright",c("human","lightning"),fill = c("black","grey"), bty="n",cex=1.4)
text(9,190,"b) Northern California",cex=1.7)
dev.off() 

ta = table(out2$human,out2$mont)

pdf("/Users/stijnhantson/Documents/Documents/articulos/en_proceso/VIIRS_ros/S9c_v1.pdf", width = 7, height = 6)
ps = barplot(ta, beside=TRUE, ylab="Number of fires",xpd=T,xaxt='n',xlab= "Month", ylim=c(0,200), axis.lty=1,cex.lab = 1.5,cex.axis = 1.4 )
axis(1,at=c(2,5,8,11,14,17,20,23,26,29,32,35), labels =c(1:12),xlim=c(0,36),xpd=F,cex.lab = 1.4,cex.axis = 1.3 )
legend("topright",c("human","lightning"),fill = c("black", "grey"), bty="n",cex=1.4)
text(11.2,190,"c) Mediterranean California",cex=1.7)
dev.off() 

ps = barplot(ta, beside=TRUE, ylab="Number of fires",xpd=T,xaxt='n',xlab= "Month", ylim=c(0,200), axis.lty=1,cex.lab = 1.5,cex.axis = 1.4 )
axis(1,at=c(2,5,8,11,14,17,20,23,26,29,32,35), labels =c(1:12),xlim=c(0,36),xpd=F,cex.lab = 1.4,cex.axis = 1.3 )
legend("topright",c("human","lightning"),fill = c("black", "grey"), bty="n",cex=1.4)


```


############  analysis of MTBS temporal trend   ###############
```{r}
library(raster)
library(rgeos)
mtbs_dir="/Users/stijnhantson/Documents/data/MTBS/DATA/"
med=shapefile("/Users/stijnhantson/Documents/data/veg_california/med_cal.shp")
north=shapefile("/Users/stijnhantson/Documents/data/veg_california/north_cal.shp")

year=1984
k=0
dnbr_all_med=0
rdnbr_all_med=0
dnbr_all_nor=0
rdnbr_all_nor=0

len_dnbr_med=0
len_rdnbr_med=0
len_dnbr_nor=0
len_rdnbr_nor=0
for (year in 1984:2017){
  print(year)
  k=k+1
mtbs_dir_year = paste(mtbs_dir,year,"/",sep="")
bndy_list = list.files(mtbs_dir_year, pattern = "burn_bndy.shp$", recursive = T, full.names=T)
shapefile_list <- lapply(bndy_list, shapefile)
fires <- do.call(rbind, shapefile_list)
fires=gUnaryUnion(fires)
fire_north = intersect(fires,north)
fire_med = intersect(fires,med)

dnbr = raster(paste(mtbs_dir,year,"_dnbr.tif",sep=""))
  dnbr[dnbr < -2000] <- NA
  rdnbr = dnbr = raster(paste(mtbs_dir,year,"_rdnbr.tif",sep=""))
  rdnbr[rdnbr < -2000] <- NA
  
dnbr_ext_med = extract(dnbr,fire_med)
dnbr_ext_nor = extract(dnbr,fire_north)

rdnbr_ext_med = extract(rdnbr,fire_med)
rdnbr_ext_nor = extract(rdnbr,fire_north)

dnbr_all_med[k]=mean(unlist(dnbr_ext_med),na.rm=T)
rdnbr_all_med[k]=mean(unlist(rdnbr_ext_med),na.rm=T)
dnbr_all_nor[k]=mean(unlist(dnbr_ext_nor),na.rm=T)
rdnbr_all_nor[k]=mean(unlist(rdnbr_ext_nor),na.rm=T)

len_dnbr_med[k] = length(unlist(dnbr_ext_med))
len_rdnbr_med[k] = length(unlist(rdnbr_ext_med))
len_dnbr_nor[k] = length(unlist(dnbr_ext_nor))
len_rdnbr_nor[k] = length(unlist(rdnbr_ext_nor))
removeTmpFiles(0)
gc()
}


```



When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Cmd+Shift+K* to preview the HTML file). 

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.



```






